{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "V2xF4Fe_V2BC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "V2xF4Fe_V2BC",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "c223c3a2-acc9-44e3-ef16-920e91a34c1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets\n",
      "  Downloading datasets-2.19.2-py3-none-any.whl (542 kB)\n",
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      "\u001b[?25hCollecting openai\n",
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      "Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai)\n",
      "  Downloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
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      "\u001b[?25hCollecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->openai)\n",
      "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
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      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
      "Installing collected packages: xxhash, requests, h11, dill, multiprocess, httpcore, httpx, openai, datasets\n",
      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.31.0\n",
      "    Uninstalling requests-2.31.0:\n",
      "      Successfully uninstalled requests-2.31.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed datasets-2.19.2 dill-0.3.8 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 multiprocess-0.70.16 openai-1.34.0 requests-2.32.3 xxhash-3.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1LGtOjMhDcAM",
   "metadata": {
    "id": "1LGtOjMhDcAM"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from tqdm.auto import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# suppress sklearn warning\n",
    "import warnings; warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.metrics import log_loss\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.calibration import calibration_curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4kBCEkfy0ADZ",
   "metadata": {
    "id": "4kBCEkfy0ADZ"
   },
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "from google.colab import userdata\n",
    "\n",
    "client = OpenAI(api_key=userdata.get('OPENAI_API_KEY'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "-sseq16610ws",
   "metadata": {
    "id": "-sseq16610ws"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pdjYRy4I5wdC",
   "metadata": {
    "id": "pdjYRy4I5wdC"
   },
   "outputs": [],
   "source": [
    "# Importing the numpy library to perform mathematical operations\n",
    "import numpy as np\n",
    "\n",
    "# Define a function to run the fine-tuned model and get the model's response\n",
    "def run_ft_model(review, ft_id, system='', chat=False, **kwargs):\n",
    "    # Use OpenAI's API to create a completion using the fine-tuned model\n",
    "    if chat:\n",
    "        completion = client.chat.completions.create(\n",
    "            model=ft_id,\n",
    "            messages=[\n",
    "                {\"role\": \"system\", \"content\": system},\n",
    "                {\"role\": \"user\", \"content\": review}\n",
    "            ] if system else [{\"role\": \"user\", \"content\": review}],\n",
    "            max_tokens=1,\n",
    "            logprobs=True,                 # Request logprobs\n",
    "            top_logprobs=20,               # Request the top 20 log probabilities for the completion,\n",
    "            **kwargs\n",
    "        )\n",
    "        text = completion.choices[0].message.content.strip()\n",
    "        logprobs = completion.choices[0].logprobs.content[0].top_logprobs\n",
    "        return text, {logprob.token: np.round(np.exp(logprob.logprob),5) for logprob in logprobs}\n",
    "    else:\n",
    "        completion = client.completions.create(\n",
    "            model=ft_id,                   # Specify the fine-tuned model ID\n",
    "            prompt=review,                 # Format the review with the prompt structure\n",
    "            max_tokens=1,                  # Limit the response to 1 token (useful for classification tasks)\n",
    "            logprobs=20,                   # Request logprobs\n",
    "\n",
    "        )\n",
    "\n",
    "        # Extract the model's completion text and strip any extra whitespace\n",
    "        text = completion.choices[0].text.strip()\n",
    "\n",
    "        # Convert the log probabilities to regular probabilities using the exponential function\n",
    "        # This provides a clearer understanding of the model's confidence in its responses\n",
    "        probs = {k: np.exp(v) for k, v in completion.choices[0].logprobs.top_logprobs[-1].items()}\n",
    "\n",
    "        return text, probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "i1sjEnHSzl4m",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i1sjEnHSzl4m",
    "outputId": "55bd3906-8dac-4bd8-a103-2cbdd39a1b1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0',\n",
       " {'0': 0.6913066337680023,\n",
       "  '4': 0.18054803765941707,\n",
       "  '1': 0.07534118109321017,\n",
       "  '2': 0.030275447756724812,\n",
       "  '3': 0.022176032287717223})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# babbage for one epoch\n",
    "run_ft_model('I hated it\\n###\\n', 'ft:babbage-002:personal::9PVvOEA2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eDcjCX62l7cc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eDcjCX62l7cc",
    "outputId": "f4590a73-5c47-47db-9629-0f1d9ea9c22e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0',\n",
       " {'0': 0.84944,\n",
       "  '4': 0.07308,\n",
       "  '1': 0.04314,\n",
       "  '2': 0.0213,\n",
       "  '3': 0.01293,\n",
       "  '5': 3e-05,\n",
       "  '8': 1e-05,\n",
       "  '7': 1e-05,\n",
       "  '9': 1e-05,\n",
       "  '6': 1e-05,\n",
       "  '00': 0.0,\n",
       "  '10': 0.0,\n",
       "  '01': 0.0,\n",
       "  '100': 0.0,\n",
       "  '000': 0.0,\n",
       "  '-': 0.0,\n",
       "  ' ': 0.0,\n",
       "  '04': 0.0,\n",
       "  '02': 0.0,\n",
       "  '20': 0.0})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gpt-3.5 no system prompt\n",
    "run_ft_model('I hated it\\n###\\n', 'ft:gpt-3.5-turbo-0125:personal::9PdLZ86e', chat=True, system='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "MMZfHMoz5wX3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MMZfHMoz5wX3",
    "outputId": "2c101773-a923-481e-cde1-e98af078a581"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0',\n",
       " {'0': 0.99859,\n",
       "  ' ': 0.00112,\n",
       "  '1': 6e-05,\n",
       "  '-': 4e-05,\n",
       "  'Rating': 3e-05,\n",
       "  'I': 2e-05,\n",
       "  '\\n': 2e-05,\n",
       "  'This': 1e-05,\n",
       "  '\\n\\n': 1e-05,\n",
       "  'Given': 1e-05,\n",
       "  'Rated': 1e-05,\n",
       "  'The': 1e-05,\n",
       "  'Star': 1e-05,\n",
       "  'It': 0.0,\n",
       "  ' \\n\\n': 0.0,\n",
       "  '(': 0.0,\n",
       "  'Score': 0.0,\n",
       "  'Review': 0.0,\n",
       "  ' \\n': 0.0,\n",
       "  '2': 0.0})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_ft_model(\n",
    "    'Please classify this app review with 0-4 where 0 means they hated it, 2 means they kind of liked it, and 4 means they loved it.\\n\\ntReiew: I hated it\\nStar Rating:',\n",
    "    'gpt-3.5-turbo-0125', chat=True, system='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1O4P5cNU1AV_",
   "metadata": {
    "colab": {
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      "b46c307ccaa8485a8d5fe93807c7b6bb",
      "f31bfecfbeae4a51b50efe6160fdaf7b",
      "bd2c4a4b94d9466abbb35e58fa971ce2",
      "d3e918f423134b4cb8d23451fe3c8585",
      "20c3d268b68b44be888f6d0047d42d32",
      "91297817396943d2be8ea5f3385ca678",
      "e885dd30643642d3a5b27834b3fb96a2",
      "821b3a4036a94a25b63391776240788e",
      "50ec2879a7d7491eaa86f8e1aa12b2ce",
      "6e214d4d59a846ecb32391dac4401352",
      "06cb0cddd75d4c7aa91ff79975b605b4",
      "70ab275c8000481fa8b8b2ed2f611a92",
      "20e856611a8a4fac8c5b2f5a5d5f10dd",
      "a75341216e0448c0a33b61dad4a7602e",
      "e66ddb09b6494f1798dae0d32b118060",
      "17d4bc81fb9b4afd9b8083ab618788a2",
      "9ab357a97b16486d8dcbcd0657d4d760"
     ]
    },
    "id": "1O4P5cNU1AV_",
    "outputId": "dccccf71-8b22-4b62-96eb-dc02edb9fa66"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df736ce1add84be88794d6552f909484",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/5.42k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97176468125544c690a0a17a058dbab6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/13.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1012ec29c2fa47639587047a06a5412b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/288065 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e1a989c884c42c7bb0a9044aaffa3db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Stringifying the column:   0%|          | 0/288065 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e885dd30643642d3a5b27834b3fb96a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Casting to class labels:   0%|          | 0/288065 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['package_name', 'review', 'date', 'star'],\n",
       "    num_rows: 288065\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import ClassLabel\n",
    "dataset = load_dataset('app_reviews', split='train')\n",
    "dataset = dataset.class_encode_column('star')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pFtOfnNZ1ATW",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pFtOfnNZ1ATW",
    "outputId": "f129f8eb-ab24-4ec3-eba3-83c33c1f9f05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 172839\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 57613\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 57613\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEED = 42\n",
    "dataset = dataset.train_test_split(test_size=0.2, seed=SEED, stratify_by_column='star')\n",
    "df = dataset['train'].train_test_split(test_size=.25, seed=SEED, stratify_by_column='star')\n",
    "dataset['train'] = df['train']\n",
    "dataset['val'] = df['test']\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sdaxgu8nHOEz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sdaxgu8nHOEz",
    "outputId": "b0738007-ccd8-402a-d405-f46232ad9104"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57613"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "DgSCiyubv0bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DgSCiyubv0bf",
    "outputId": "745b229f-317a-4f7f-e9c8-2f47948ab874"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5762"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sample = dataset['test'].train_test_split(test_size=.1, seed=SEED, stratify_by_column='star')['test']\n",
    "len(test_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "v0pf1xp2FXfY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v0pf1xp2FXfY",
    "outputId": "f5a5747f-89c4-48ca-8e1e-d5d09a39af45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'package_name': 'net.androgames.level',\n",
       " 'review': 'Does what it should.',\n",
       " 'date': 'February 18 2017',\n",
       " 'star': 4}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sample[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VtqR5oRD2rkR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "e038707ef2c2490995b3d422f7d3e40b",
      "dd01ba196b2f47bba20cb5bb1de685fd",
      "3b734b86437341668f061dfa6db4f112",
      "012347d9b4724e159a2a7a2b42ec307c",
      "ac72578e90224e6ea50ddad23cb710a5",
      "855a53d946474d36a7abf2810b47d662",
      "26beeb11d24d4e06a15f0d344ed8e64e",
      "7bb638c32e6e4a6f84654fecff852650",
      "7db7f7b5564d42b69bb94d1d4b0df1a4",
      "9bcfbc3e87af498091ead3eae97cf37c",
      "f4c683c2f6fd4f5892f4f62190309530"
     ]
    },
    "id": "VtqR5oRD2rkR",
    "outputId": "a52ec80f-b328-4cb7-eb8e-dddfde039f66"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e038707ef2c2490995b3d422f7d3e40b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5762 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'class_0_probs': [],\n",
    "    'class_1_probs': [],\n",
    "    'class_2_probs': [],\n",
    "    'class_3_probs': [],\n",
    "    'class_4_probs': [],\n",
    "    'actual_labels': [],\n",
    "    'predicted_token': []\n",
    "}\n",
    "\n",
    "for test in tqdm(test_sample):\n",
    "    _data = {}\n",
    "    try:\n",
    "        predicted_label, probs = run_ft_model(test['review']+'\\n###\\n', 'ft:babbage-002:personal::9PVvOEA2')  # babbage for one epoch\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "    for key, prob in probs.items():\n",
    "        if str(key.lower().strip()) in ['0', '1', '2', '3', '4']:\n",
    "            _data[f'class_{key.lower().strip()}_probs'] = prob\n",
    "    if len(_data) >= 5:\n",
    "        for key, value in _data.items():\n",
    "            data[key].append(value)\n",
    "        data['actual_labels'].append(test['star'])\n",
    "        data['predicted_token'].append(predicted_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "FprLlZLf2o8B",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "FprLlZLf2o8B",
    "outputId": "4b1c4460-12c7-47b8-8fc8-91888a70fb28"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//rr0+uuva9OVZip4aZ5vbGxsgXo/ePBA6tevn2RlZSVZWlpKAwcOlB4+fCgB0ty5c/Ol/fzzz6WqVatKcrk83/N+ciq4JElScHCw9Oabb0pWVlaSkZGR1KpVK2nv3r350uRNBf/999/zHS/s/ouydOlSyczMrMCU9rxnU1LFTQUfPHiw1L59+xLnJwi6IpOkYkafCYLwn5aUlIS1tTVffPEFs2bNquzqCDqQnJyMm5sbX3/9Ne+//36FlBEVFYWrqyu+vr6i5UZ45sSYG0EQtDIyMgocyxtL8sYbbzzbyggVxtLSkunTp7N48eIKm921fPlyGjVqJAIboVKIlhtBELQ2bdqk3dXczMyMkydPsnXrVrp27Vro4GFBEITnkRhQLAiCVuPGjdHT0+Prr79GqVRqBxl/8cUXlV01QRCEEhMtN4IgCIIgvFTEmBtBEARBEF4qIrgRBEEQBOGl8p8bc6PRaHj48CHm5uZiMzdBEARBeEFIkkRKSgrOzs7FLmr5nwtuHj58iIuLS2VXQxAEQRCEMrh//z7VqlV7apr/XHCTtwvu/fv3tUvjC4IgCILwfFMqlbi4uJRoN/v/XHCT1xVlYWEhghtBEARBeMGUZEiJGFAsCIIgCMJLRQQ3giAIgiC8VERwIwiCIAjCS+U/N+ampNRqNTk5OZVdDUGoEPr6+igUisquhiAIQoUQwc0TJEkiKiqKpKSkyq6KIFQoKysrnJycxHpPgiC8dERw84S8wMbBwQETExPxxi+8dCRJIj09nZiYGACqVKlSyTUSBEHQLRHcPEatVmsDG1tb28qujiBUGGNjYwBiYmJwcHAQXVSCILxUxIDix+SNsTExMankmghCxct7nYuxZYIgvGxEcFMI0RUl/BeI17kgCC8r0S0lCIIgvLTUGjX+Mf7Epsdib2JPc4fmKOQvTzespFaTfvESqthY9OztMWnZApnoZq7clpsTJ07g7e2Ns7MzMpmMXbt2FXvN8ePHad68OYaGhtSuXZtNmzZVeD1fJiV9zoIgCC+6I2FH8PrDi/cOvceMf2bw3qH38PrDiyNhRyq7ajqh9PPjbmdPwocP5+HUqYQPH87dzp4o/fwqu2qVrlKDm7S0NJo0acKaNWtKlD4kJISePXvSsWNHAgICmDRpEiNHjuTQoUMVXNPSU2skzgTH82dABGeC41FrpAovMyoqivHjx+Pm5oahoSEuLi54e3tz9OjRCi+7JCRJ4tNPP6VKlSoYGxvj6elJUFBQZVdLEISX0JGwI/gc9yE6PTrf8Zj0GHyO+7zwAY7Sz4+IiZNQRUXlO66KjiZi4qT/fIBTqd1S3bt3p3v37iVO/+233+Lq6sqSJUsAqFevHidPnmTZsmV4eXlVVDVL7eD1SObvCSQyOVN7rIqlEXO969OtYcVMuw0NDaVdu3ZYWVmxePFiGjVqRE5ODocOHWLs2LHcunWrQsotja+//pqVK1fy008/4erqypw5c/Dy8iIwMBAjI6PKrp4gCC8JtUbNl+e/RKLgl0oJCRkyvjr/FR1dOr6QXVSSWk30wkUgFfKlWZJAJiN64SLMO3f+z3ZRvVADis+cOYOnp2e+Y15eXpw5c6bIa7KyslAqlfl+KtLB65GM3uKfL7ABiErOZPQWfw5ej6yQcseMGYNMJuP8+fMMGDAADw8PGjRogI+PD2fPni3yuhkzZuDh4YGJiQlubm7MmTMn3+yZK1eu0LFjR8zNzbGwsKBFixZcvHgRgLCwMLy9vbG2tsbU1JQGDRqwf//+QsuRJInly5cze/Zs+vTpQ+PGjdm8eTMPHz4U3WSCIOiUf4x/gRabx0lIRKVH4R/j/wxrpTvpFy8VaLHJR5JQRUWRfvHSs6vUc+aFGlAcFRWFo6NjvmOOjo4olUoyMjK0a3c8btGiRcyfP7/MZUqSREaOukRp1RqJubtvFPJdASRABszbHUi72nYo5MXPVDHWV5RoRktCQgIHDx5kwYIFmJqaFjhvZWVV5LXm5uZs2rQJZ2dnrl27xqhRozA3N2f69OkADB06lGbNmrFu3ToUCgUBAQHo6+sDMHbsWLKzszlx4gSmpqYEBgZiZmZWaDkhISFERUXlC04tLS1p3bo1Z86c4a233ir2PgVBEEoiNj1Wp+meN6rYktW7pOl0ITY2FhMTk0I/gyrDCxXclMXHH3+Mj4+P9nelUomLi0uJr8/IUVP/U92M6ZGAKGUmjeaVrC808DMvTAyK/1909+5dJEmibt26pa7T7Nmztf+uWbMmU6dOxdfXVxvchIeHM23aNG3e7u7u2vTh4eEMGDCARo0aAeDm5lZkOVGPvmUUFpxGPe0biCAIQinZm9jrNN3zRs++ZPUuabryOnnyJIMHD6Zbt2788MMPz6TM4rxQwY2TkxPR0fmbGqOjo7GwsCi01QbA0NAQQ0PDZ1G9SiMV1u9aQtu2bWPlypUEBweTmpqKSqXCwsJCe97Hx4eRI0fy888/4+npycCBA6lVqxYAEyZMYPTo0fj5+eHp6cmAAQNo3Lhxue9HEAShPJo7NMfRxLHIrikZMhxNHGnu0PwZ10w3TFq2QG5piSY5ufAEMhl6jo6YtGyhPaTWSJwPSSAmJRMHcyNaudqUqAehJHJycoiMjOT06dMkJydjaWmpk3zL44UKbtq0aVNgTMfhw4dp06ZNhZVprK8g8LOSDVY+H5LAiI0Xik236d1XaOVqU6KyS8Ld3R2ZTFbqQcNnzpxh6NChzJ8/Hy8vLywtLfH19dUO2AaYN28eQ4YMYd++fRw4cIC5c+fi6+tLv379GDlyJF5eXuzbtw8/Pz8WLVrEkiVLGD9+fIGynJycgNxg9PG9jKKjo2natGmp6i0IgvA0CrmCma1mMvn45ALnZOR+oM9oNeOFHEwMkHnzFpq0tMJPPhrK4PjJx9rBxBUxyUWSJO2wiY4dO7Jz5046d+5c5NCEZ61SBxSnpqYSEBBAQEAAkDsuIyAggPDwcCC3S2nYsGHa9B999BH37t1j+vTp3Lp1i7Vr1/Lbb78xeXLBF7CuyGQyTAz0SvTTwd2eKpZGFBULy8h9QXVwty9RfiVdQdbGxgYvLy/WrFlDWiEv+KJ2OD99+jQ1atRg1qxZtGzZEnd3d8LCwgqk8/DwYPLkyfj5+dG/f382btyoPefi4sJHH33Ejh07mDJlCuvXry+0LFdXV5ycnPJNS1cqlZw7d65Cg1NBEP6balrU1AYyj3M0cWTpG0vxrOFZyFXPP1VCAg/GjweVCqMGDdB7oqtfz9GRqiuWY9G1K1Axk1z++usvWrduTXx8vPZYnz59npvABiq55ebixYt07NhR+3ve2Jjhw4ezadMmIiMjtYEO5H5A7tu3j8mTJ7NixQqqVavGhg0bnptp4Aq5jLne9Rm9xR8Z5BtYnPcnNte7vs6aAh+3Zs0a2rVrR6tWrfjss89o3LgxKpWKw4cPs27dOm7evFngGnd3d8LDw/H19eWVV15h37597Ny5U3s+IyODadOm8eabb+Lq6sqDBw+4cOECAwYMAGDSpEl0794dDw8PEhMTOXbsGPXq1Su0fjKZjEmTJvHFF1/g7u6unQru7OxM3759df48BEH4b1t1eRUSEp1dOjO0/tCXYoViKSeHiEmTUUVGYlCzJtU3bURuYlLkCsVqjcT8PYFPneQyf08gXeo7lfhzSaVSMW7cOG7evMn8+fNZuXKlzu5Plyo1uHnjjTeeOl6ksNWH33jjDS5fvlyBtSqfbg2rsO5/zQs0ATpV8Do3bm5u+Pv7s2DBAqZMmUJkZCT29va0aNGCdevWFXpN7969mTx5MuPGjSMrK4uePXsyZ84c5s2bB4BCoSA+Pp5hw4YRHR2NnZ0d/fv3184+U6vVjB07lgcPHmBhYUG3bt1YtmxZkXWcPn06aWlpfPDBByQlJdG+fXsOHjwo1rgRBEGnrsRe4a/7fyGXyZnQfAJuVkVPdniRxHzzDennzyM3MaHamtUozM0BMG3dKl+65PQcQuLTOBwYVaDF5nESEJmcyfmQBNrUsi1RHfT09Ni6dSvffvstX375ZZnvpaLJpPKMRn0BKZVKLC0tSU5OzjdwFiAzM5OQkBBcXV3L/YFbkYO3BEEXdPl6F4TnhSRJvO/3PheiLtCvdj8+a/dZZVdJJ5L//JOHM2YCUG31Kmj/OqFx6YTEpxEal/uT9+/E9JxicstvxVtN6dO0apHn/fz8yM7OplevXuW6h/J62uf3k16oAcUvEoVcVuJIWBAEQdCNMw/PcCHqAgZyA0Y3GV1h5TyLL7BpWSpC4tJ4eOEyznM+RQEca+XNj2cl4o48fUkRB3NDbEwNuBWVUmw5DuZFf7k5cOAAPXr0wMrKioCAAGrUqFHa26gUIrgRBEEQXgoaScNy/+UAvFX3LaqYVcwwAF3OPsrIVhMan7/lJa9FJjYlC8usVFYcX44iJ5tzjvVYXKUDUmo2AHZmBtS0NaWmnSmudqaP/m1CTVtTTA31UGsk2n/1F1HJmYWOu5GRO2TiabN3PT09efXVV2natCkODg6lurfKJIIbQRAE4aXgF+bHzYSbmOqbMrLRyAopI2/20ZPBQt7so3X/a14gwMnMURMWn05IXBph8WmExqcR8iiIiVIWPSZGrlEzx/8XHDOSSLZ1ImPqHFZUd8T1URBjbqT/1LqWdZLLxYsXadGiBTKZDH19ff76668i15J7XongRhAEQXjh5WhyWH15NQDDGwzH2sha52UUN/sI4OMd1wiLTycsIV07FiZSmVnoHpd5LI31c1tfbE3ytcJYb1pLWnQQchMTmv20nldr1y51nUs7yWXWrFksXLiQdevW8dFHHwG8cIENiOBGEARBeAnsuruLMGUYNkY2DKs/rPgLyuB8SMJTZx8BJKbnsOhAwQVVzY30Hus6MsXVzoQatqa42ppibWpQIH3ynj08/OVnAKp8uQjDMgQ2ebo1rEKX+k4lGiNkY5PbRRUcHFzm8p4HIrgRBEEQXmgZqgy+DfgWgA8af4CpfsVs3hiT8vTAJk9TFyva17bTBjE1bU2xMTUo8cKsmYGBRM6eA4DtRx9qF+Qrj6dNcsnOzsbAIDfA8vHxoXXr1rRv377cZVYmEdwIgiAIL7Stt7YSkxGDs6kzAz0GVlg5T5tV9LgZ3eqWebasKjGRB+PGI2VlYfr6a9gXsp2NrmRnZzN9+nSuXLnCkSNHUCgUyGSyFz6wgUrefkEQBEEQyiM5K5kN1zYAMLbZWAwUBbt4dKWVqw1VLIsOcPK22CnJ3oGFkVQqIib7kPPwIfo1qlN18WLtasMV4f79+/zwww8cP36cI0eOVFg5lUEEN4IgCMILa9ONTaRkp1DbqjY9XXtWaFkKuYwPXy98tWNdbLETs2Qp6WfPIjMxodqqVSiKWaiuvGrVqsXGjRv5888/n5ttjHRFBDf/MTKZjF27dlV2NQRBEMotNj2WX27+AsCEZhMqfM8otUZid8BDAAwU+T8+nSyNCp0GXlLJe/aS8GhTYudFizDy8ChfZQGNRiLidiJ3LkQRcTuR9PQMfHx8uHXr3wHPb775Jr179y53Wc8bMeamomjUEHYaUqPBzBFqtIUK/sOLiopiwYIF7Nu3j4iICBwcHGjatCmTJk2ic+fOFVp2SezYsYNvv/2WS5cukZCQwOXLl2natGllV0sQhBfUd1e/I0OVQRP7Jrzh8kaFl/fDyXv4hydhZqjH/okdiEjM0MkKxZk3bxI559EA4g8+wMKr/AOIgy/H8M+2INKSsrTHdl5Yy1H/Pzh69Cj+/v4oKrDLq7KJ4KYiBO6GgzNA+fDfYxbO0O0rqF8xEXJoaCjt2rXDysqKxYsX06hRI3Jycjh06BBjx47NF6lXlrS0NNq3b8+gQYMYNWpUZVdHEIQX2H3lff648wcAk5pPKvFMpLK6G5PCN353AJjTqx7VbUyobmNS7ny1A4gzMzHt0AH7iRPKnWfw5RgOfne9wPHODd4iMNSfiaNmvtSBDYhuKd0L3A2/Dcsf2AAoI3OPB+6ukGLHjBmDTCbj/PnzDBgwAA8PDxo0aICPjw9nz54t8roZM2bg4eGBiYkJbm5uzJkzh5ycfzddu3LlCh07dsTc3BwLCwtatGjBxYsXAQgLC8Pb2xtra2tMTU1p0KAB+/fvL7Ksd955h08//RRPT0/d3bggCP9JqwNWo5JUtKvajpZOLSu0LJVaw5TfrpCt0vBGHXsGtXTRSb6SSkWEjw85ERHoV69O1W/KP4BYo5H4Z1sQANmqLALvX9CeszCx4eM3v0cRUR2N5uXeM1u03BRHkiAnvWRpNWo4MB2KXL9Sltui4/ZGybqo9E2gBN9GEhISOHjwIAsWLMDUtOD6DlZWVkVea25uzqZNm3B2dubatWuMGjUKc3Nzpk+fDsDQoUNp1qwZ69atQ6FQEBAQgL5+7pLfY8eOJTs7mxMnTmBqakpgYCBmZmbF35cgCEI53Eq4xf6Q3C9SE5tNrPDyvjtxjysPkjE30uPL/o111koUs3QZ6WceDSBevQqFpWW584wMSiItKYv0rFSW755EVGIYk3ovx82pAQBymZzUxCwig5KoWkf3qzg/L0RwU5ycdFjorKPMpNwWnS9LGPV/8hAMil+M6u7du0iSRN26dUtdo9mzZ2v/XbNmTaZOnYqvr682uAkPD2fatGnavN3d3bXpw8PDGTBgAI0aNQLAza3wWQSCIAi6tNJ/JQDda3annm29Ci3rVpSS5Udyu6PmeTfA6SlTwUsjed8+En78EQDnhQt0MoAYIE2ZO8bG2MCUKtY1SclIRKXJKTLdy0oENy8B6WmblhRj27ZtrFy5kuDgYFJTU1GpVFg8Nv3Qx8eHkSNH8vPPP+Pp6cnAgQOpVasWABMmTGD06NH4+fnh6enJgAEDaNy4cbnvRxAEoSgXoy7yT8Q/6Mn0GNdsXIWWlaPWMPX3K+SoJTzrOdK/eVWd5Jt56xaRs3K/WNqOGoVFt246yTctLY2EKCWQOzP2rdcmk52TiaVpwQUFTS0MdVLm80oEN8XRN8ltQSmJsNPwy5vFpxu6PXf2VEnKLgF3d3dkMlmpBw2fOXOGoUOHMn/+fLy8vLC0tMTX15clS5Zo08ybN48hQ4awb98+Dhw4wNy5c/H19aVfv36MHDkSLy8v9u3bh5+fH4sWLWLJkiWMr8AVNQVB+O+SJIkV/isA6O/en+oW1Su0vLXHgrkeocTKRJ+F/RvqpDsq3wDi9u2xn6SbbjX/CwH07/smbrZNGdB2DJDbemNcSOu/mbUhVdytdFLu80oMKC6OTJbbNVSSn1qdcmdFUdQfgAwsquamK0l+JfxDsrGxwcvLizVr1pCWllbgfFJSUqHXnT59mho1ajBr1ixatmyJu7s7YWFhBdJ5eHgwefJk/Pz86N+/PxsfrcUA4OLiwkcffcSOHTuYMmUK69evL1GdBUEQSuvvB38TEBuAkcKID5t8WKFl3XiYzKq/cgfmzu/doMRbLzyNpFbzcMpUch48QN/FRScDiCVJ4u6lGDYtPEDYw2D8g49jW1v/qde0H+SOvIzT1l8UIrjRJbkid7o3UDDAefR7ty8rZL2bNWvWoFaradWqFX/88QdBQUHcvHmTlStX0qZNm0KvcXd3Jzw8HF9fX4KDg1m5ciU7d+7Uns/IyGDcuHEcP36csLAwTp06xYULF6hXL7ePe9KkSRw6dIiQkBD8/f05duyY9lxhEhISCAgIIDAwEIDbt28TEBBAVFSUDp+EIAgvI7VGrW21GVpvKA4mDhVWVrYqd3aUSiPRvaETvZvoZtxl7LJlpJ0+jczYmGqrV6N4ymSPkkhLzuLgd9c5tP46dRxbM6r3J5w8fpa3pnag24cNMbXK3/VkZm1Itw8bUqtZxT2754b0H5OcnCwBUnJycoFzGRkZUmBgoJSRkVG+Qm78KUlL6krSXIt/f5bUyz1egR4+fCiNHTtWqlGjhmRgYCBVrVpV6t27t3Ts2DFtGkDauXOn9vdp06ZJtra2kpmZmTR48GBp2bJlkqWlpSRJkpSVlSW99dZbkouLi2RgYCA5OztL48aN0z6fcePGSbVq1ZIMDQ0le3t76Z133pHi4uKKrN/GjRslcqeN5fuZO3duBTwNoTg6e70LwjOw++5uqeGmhlKbX9tISZlJFVrWN4duSTVm7JWafeYnxaZk6iTP5P37pcA6daXAOnWl5P37y5WXRqORdv50RGrk2kZa/O5uae3ov6SzfwZLqmx1vnRqtUZ6cCtBun0+UnpwK0FSqzXlKreyPe3z+0kySSrHaNQXkFKpxNLSkuTk5HwDZwEyMzMJCQnB1dUVI6NyNkFWwgrFglAaOn29C0IFylHn4L3Lm4jUCCY1n8T7jd6vsLKuPkii39rTqDUSa4Y0p2fjsm2n8LjM27cJfettpIwMbEe+j8PUqWXOSxmfwV8/B/LR/H5EJ93H69WBbPn9B+yqmZe7ns+7p31+P0kMKK4ocgW4dqjsWgiCILzwfr/zOxGpEdgb2zOk3pAKKydLpWbKb1dQayR6Na6ik8BGnZSUO4A4IwPTtm2xnzy5TPlIGonrJyI4vTMYVZaadzpN50LkbjbvXI2d48sf2JSWCG4EQRCE51Z6TjrfXf0OgI+afISxnnGFlbXscBBBManYmRnyeZ+G5c5PUquJmDKVnPv30a9WjapLl5RpAHFSdDobFu3kwb0Y3J2bUKW2JUPmvYu1U8VOhX+RieBGEARBeG79HPgzCZkJuJi70M+9X4WV4x+eyPcnggFY2K8h1qYG5c4zdvkK0k6dQmZklLsCcSkHEGvUGgKO3OfndTtYvWcGxoZm7Nx8mE79myN7yWc7lZcIbgRBEITnUmJmIptubAJgfLPx6MufPsW5rDJz1Ez9/QoaCfo1q0rXBk7lzlN58CDxj5bGqLLgC4xKuYJ83INU/tp8k9jwFKrb1aWqYw0aNq1Ls06uIrApARHcCIIgCM+lH679QGpOKnVt6uJV06vCylnid5t7sWk4mBsyz7tBufPLvH2Hhx9/AoDNe+9h2bNnia9V52i4eCAUv9/OYmPmhKGJHp3erMs7X57B1ta2wnc/f1mI4EYQBEF47kSlRbH11lYAJjafiFxWMcuyXQhNYMPJEAC+HNAIS5PytQ6pk5N5MD5vAHEbHHxKPoA4KiSZYz/f4o9Dm9l19nsmD/uMeV/5YGr5cm+VUBFEcCMIgiA8d9ZdWUe2JpuWji1p59yuQspIz1Yx7fcrSBIMbFGNTnUdy5WfpFYTMXUaOeHh6FetivOSJcj0iv+YzclWc273Pa4evY8kQbaUjlqjIo47IrApIxHcCIIgCM+Ve8n32HV3F5DbalNRXTFfH7xNaHw6VSyNmONdv9z5xa5YSdo//2gHEOtZWxd7TcTtRP7acovkmHRkMhkerR0Z/tVaDh7uyYABA8pdp/8qEdwIgiAIz5XVl1ejkTR0dOlIU4emFVLGmeB4Np0OBeCrAY2xMCpfd5Ty4CHiv/8egCpffIHRU7aiAcjOUHF6x12unXjA0Su/8SDxNr//vh3XxvYAvPlmCTZhFook9pb6j5HJZOzatauyqyEIglCo63HXORx2GBkyJjSbUCFlpGWpmLb9CgBvt6rOax725cov884dHn7yaADxu+9i2evpA4hDr8Wx9bNz3PjnIXHKh+z3/4lLd05wPexsueoh/EsENxVErVFzIeoC++/t50LUBdQadYWXGRUVxfjx43Fzc8PQ0BAXFxe8vb05evRohZddnJycHGbMmEGjRo0wNTXF2dmZYcOG8fDhw8qumiAIz5G8zTG9a3lT27p2hZSx6MBNHiRmUNXKmFk9n97CUhx1cnLuCsTp6Zi0eRWHKT5Fps1MzeHwxhvsW3OV1MQsLOyNGTXPm7Vr1/D999/Tq1evctVF+JfolqoAR8KO8OX5L4lOj9YeczRxZGarmXjW8KyQMkNDQ2nXrh1WVlYsXryYRo0akZOTw6FDhxg7diy3bt2qkHJLKj09HX9/f+bMmUOTJk1ITExk4sSJ9O7dm4sXL1Zq3QRBeD6ceXiGs5Fn0ZPrMabpmAop42RQHFvOhgOw+M3GmBmW/WNQUquJmPZoALGzM1WXLi10ALEkSQT7x3LC9zZpyiz+uvIbQ4a9Td+RrdA3UDCyzsgy10EonGi50bEjYUfwOe6TL7ABiEmPwee4D0fCjlRIuWPGjEEmk3H+/HkGDBiAh4cHDRo0wMfHh7Nni27qnDFjBh4eHpiYmODm5sacOXPIycnRnr9y5QodO3bE3NwcCwsLWrRooQ1GwsLC8Pb2xtraGlNTUxo0aMD+/fsLLcfS0pLDhw8zaNAg6tSpw6uvvsrq1au5dOkS4eHhun0YgiC8cCRJ0rbaDK4zmKpmVXVeRkpmDtMfdUcNa1ODtrXtypVf7KpVpJ34B5mhYZEDiNOSszj43XUOrb9ORkoOB69tZNe59SzZ/DEy+X9q3+pnSrTcFEOSJDJUGSVKq9aoWXR+ERIFX7B5x748/yWtnVqjKMEO4cZ6xiWaJZCQkMDBgwdZsGABpqamBc5bPWXJb3NzczZt2oSzszPXrl1j1KhRmJubM336dACGDh1Ks2bNWLduHQqFgoCAAPT1cwfejR07luzsbE6cOIGpqSmBgYGYmZkVW988ycnJyGSyp9ZPEIT/hiPhR7gRfwNjPWNGNRpVIWUs2HeTh8mZVLcxYUa30q0Y/CSlnx/x3+bueeX4+efEK6qQdiEKUwtDqrhbIZPBrTNRnNoeRFa6CrlcRvNuNeg25XMC3jjOhx9+iKIM+0wJJSOCm2JkqDJo/WtrneUXnR5NW9+2JUp7bsg5TPRNik139+5dJEmibimX9waYPXu29t81a9Zk6tSp+Pr6aoOb8PBwpk2bps3b3d1dmz48PJwBAwbQqFEjANzc3EpcbmZmJjNmzODtt98udut6QRBebiqNipX+KwEY3mA4tsa2Oi/j+O0YfC/cRyaDbwY2wbSU3VGSWk36xUuoYmORsrOI/PwLADIGTuLP0zak7b+sTWtiYYCJhQFxD1LRaNSk6j1k1Cf9sauWu3t3cHAwRkZGurs5oQAR3LwEJKnsTZvbtm1j5cqVBAcHk5qaikqlyhds+Pj4MHLkSH7++Wc8PT0ZOHAgtWrVAmDChAmMHj0aPz8/PD09GTBgAI0bNy62zJycHAYNGoQkSaxbt67MdRcE4eWwO3g3ocpQrAytGF5/uM7zT87IYeYf1wB4t60rrVxtSnW90s+P6IWLUEVF5Tue0KgbAbHuQFa+4+nKbNKV2WSp0tl6cSHXbvnT48Om2FVrAiACm2dABDfFMNYz5tyQcyVKeyn6EmOOFj8Ibm3ntbRwbFGiskvC3d0dmUxW6kHDZ86cYejQocyfPx8vLy8sLS3x9fVlyZIl2jTz5s1jyJAh7Nu3jwMHDjB37lx8fX3p168fI0eOxMvLi3379uHn58eiRYtYsmQJ48ePL7LMvMAmLCyMv/76S7TaCMJ/XKYqk7UBawEY1WgUZgYl79ouqc/2BBKlzMTVzpRpXnVKda3Sz4+IiZPgiS+REjJumrYHJKDw4QOW1hY4VLVB764eoaGhNGnSpGw3IJSaGFBcDJlMhom+SYl+2jq3xdHEEVkRL3QZMpxMnGjr3LZE+ZV0VU4bGxu8vLxYs2YNaWlpBc4nJSUVet3p06epUaMGs2bNomXLlri7uxMWFlYgnYeHB5MnT8bPz4/+/fuzceNG7TkXFxc++ugjduzYwZQpU1j/aBfcwuQFNkFBQRw5cgRbW903PQuC8GLZdnsb0enROJk6MbjuYJ3nfyQwmj/8HyCXwTcDG2NsUPJxLpJaTfTCRQUCG4Akq9pkGVnzZGCj1qjRPFr6IzNFxVdzVnDp0iX69OlTrvsQSkcENzqkkCuY2WomQIEAJ+/3Ga1mlGgwcWmtWbMGtVpNq1at+OOPPwgKCuLmzZusXLmSNm3aFHqNu7s74eHh+Pr6EhwczMqVK9m5c6f2fEZGBuPGjeP48eOEhYVx6tQpLly4QL1HK29OmjSJQ4cOERISgr+/P8eOHdOee1JOTg5vvvkmFy9e5JdffkGtVhMVFUVUVBTZ2dk6fx6CIDz/UrJT2HBtAwBjmozBUKHbfZQS07L5eGdud9SoDm60qFG67qj0i5cKdEXlyTSwKlheaiwr90zhgP8W7TEDhSl16pSutUgoPxHc6JhnDU+WvrEUBxOHfMcdTRxZ+sbSClvnxs3NDX9/fzp27MiUKVNo2LAhXbp04ejRo0WOa+nduzeTJ09m3LhxNG3alNOnTzNnzhzteYVCQXx8PMOGDcPDw4NBgwbRvXt35s+fD4BarWbs2LHUq1ePbt264eHhwdq1awstKyIigt27d/PgwQOaNm1KlSpVtD+nT5/W/QMRBOG599ONn0jKSsLN0g3vWt46z3/enhvEpmRRy96UyV08Sn29Kja2wDEJiLFvRrBbwZaY4KhrBEdd4/i1HaRlKgEwtRAbX1YGmVSe0agvIKVSiaWlJcnJyQXGe2RmZhISEoKrq2u5B3ypNWr8Y/yJTY/F3sSe5g7NK6TFRhDKSpevd0EorbiMOHrs6EGGKoPlbyync43OOs3/4PVIPtrij1wGO8a0o6mLVanzSDt3nvDh/w5wTrKsxd1a/VBauOYekDSADB4bQnDo8q80d3sde8uqmFkb8s6CtsjlFbPx53/N0z6/nyQGFFcQhVzBK06vVHY1BEEQnkvrr64nQ5VBI7tGdKreSad5x6dmMWvndQBGv1GrTIENgEnLFug5OZGslAh260ucXe5sUIU6C5f7R0hJDGVlcjoD20/AQC+3hcar2RDt9e0HuYvAppKI4EYQBEF4ph6kPOC3O78BMLH5xBJPniipT/+8QXxaNnUczZnQ2b34C4qQnqLiXrdZ3A0BZHJkkhrnh6epGbYf/WwlvUPucS87GzMzc/q0/Eh7nZm1Ie0HuVOrmUPRmQsVSgQ3giAIwjO1NmAtKo2KNlXa0LqK7hZJBdh79SH7rkWikMtYMqgJhnqlHw6QnaHi8uFwAo6Eo8qWgwzsYgOoFbIb00db6+g5ObFk+DC+2r2bdVu+xEhlTZoyS7tCsWixqVwiuBEEQRCemTuJd9h7by+Q22qjS7EpWczZldsdNbZjbRpWtSzV9Wq1hsB/HnJhXwgZKbl77Nkap1Pj1DqcGziSOmcSMWFhtGzTBpOWLXBXKOg9YwZyuZib87wRwY0gCILwzKzyX4WERNcaXWlg10Bn+UqSxKyd10hMz6F+FQvGdaxdqmuD/WM5+2cwyTG5ewlaOhjzah83pOlDUSkjuFO/IwPfHYGlpSWXh13G9NG+UCKweT6J4EYQBEF4Ji7HXOb4g+MoZArGNRun07z/DHiIX2A0+goZ3wxsgoFeyYKOh0FJnN5xl+iQ3Knbxub6tOrlSr32zmReukh4RARyU1NeGfYOdt99i4ODAxkZJdtMWag8IrgRBEEQKpwkSSy/tByAvrX74mrpqrO8o5WZzN19A4AJndyp71z8ti4JkWmc3RVMyJU4APQM5DTrUp2mXapjYJT70Ri61Rc5YN69G1aOjhw9ehRnZ2f09fV1VnehYojgRhAEQahwJyNO4h/jj6HCkI+afFT8BSUkSRIf77hGckYOjapaMvqNWk9Nn5acxfm9Idw8+RBJAplcRv12VXillyumlv8uuLd182Y+WLmCr6pUYUS/fgDUqFFDZ/UWKpYIbgRBEIQKpZE0rPBfAcCQukNwMnXSWd7bLz3gr1sxGCjkLBnUBD1F4d1R2ZmPZkAdDkeVrQHAtYkdbfrVwtrJtED6f7ZvJ1WjYVd2DqObN9dZfYVnQwQ3/zEymYydO3fSt2/fyq6KIAj/EQdDDnI78TZm+ma81/A9neUbmZzBZ3sCAZjcxQMPR/MCaQqbAeXoakHbAbVxrm1VZN6TraywdXBkzKxPdL4Oj1DxxDDvCiKp1aSdO0/y3n2knTuPpFZXeJlRUVGMHz8eNzc3DA0NcXFxwdvbm6NHj1Z42SUxb9486tati6mpKdbW1nh6enLu3LnKrpYgCBUoR53DqsurAHi34btYGVnpJF9Jkpi+/SopWSqaVbfig9fcCpwP9o9h6/xznPC9Q0ZKDpYOxnT7oCEDprcoENhs2bKFDz74AEmSyHn4ENWFiwyxtsa2/wCd1Fd4tkTLTQVQ+vkRvXBRvt1k9ZyccPzkYyy6dq2QMkNDQ2nXrh1WVlYsXryYRo0akZOTw6FDhxg7diy3bt2qkHJLw8PDg9WrV+Pm5kZGRgbLli2ja9eu3L17F3t7+8quniAIFWBH0A4epD7A1siW/9X7n87y9b1wn3+C4jDUk/PNwCYoHls07+HdJM7suEvUvX9nQL3S05X6HZxRFNJtdffuXUaMGIFaraZnz560i4wEScKkVSsMqlXVWZ2FZ0e03OiY0s+PiImT8gU2AKroaCImTkLp51ch5Y4ZMwaZTMb58+cZMGAAHh4eNGjQAB8fH86ePVvkdTNmzMDDwwMTExPc3NyYM2cOOTk52vNXrlyhY8eOmJubY2FhQYsWLbh48SIAYWFheHt7Y21tjampKQ0aNGD//v1FljVkyBA8PT1xc3OjQYMGLF26FKVSydWrV3X3IARBeG6k56Tz7dVvAfiwyYeY6JvoJN8Hiel8sTe3O2qaVx1q2ZsBkBiVxv51V9n5jT9R95ToGchp2aMm//u8DY3eqFZoYANQu3ZtFi1axLx58+jZsyfJO3cBYCm6719Yld5ys2bNGhYvXkxUVBRNmjRh1apVtGrVqsj0y5cvZ926dYSHh2NnZ8ebb77JokWLKmxXY0mSkEq4poGkVhP9xQIobKN1SQIZRC9YiGmbNsgUxS8JLjM2LlFfb0JCAgcPHmTBggWYmhYcGGdlZVXktebm5mzatAlnZ2euXbvGqFGjMDc3Z/r06QAMHTqUZs2asW7dOhQKBQEBAdppkGPHjiU7O5sTJ05gampKYGAgZmZmxdYXIDs7m++//x5LS0uaNGlSomsEQXix/HrrV+Iy4qhqVpU33d/USZ4aTW53VFq2mpY1rHm3nStpyVlc2BtC4KlIJI2ETAb12jvT6okZUHkkSWLLli106dIFJ6fcwc3Tpk0DIN3/MtlhYchMTLDwqpiWdqHiVWpws23bNnx8fPj2229p3bo1y5cvx8vLi9u3b+PgUHDDsV9//ZWZM2fy448/0rZtW+7cucOIESOQyWQsXbq0QuooZWRwu3kLHWWW24Jz55Wig7fH1fG/hMyk+G86d+/eRZIk6tatW+oqzZ49W/vvmjVrMnXqVHx9fbXBTXh4ONOmTdPm7e7+7yZ04eHhDBgwgEaNGgHg5pa/z7swe/fu5a233iI9PZ0qVapw+PBh7OzsSl1vQRCeb8lZyfx47UcAxjUbh75CN2vD/HIujNPB8Rjpy/myd0Mu7Qvh8pH7qLJyxzW6NrHj1b61sKlS8Itenvnz5zN//nw6d+7MoUOHUDz2ZTN51y4ALLp0QV7Il0XhxVCp3VJLly5l1KhRvPvuu9SvX59vv/0WExMTfvzxx0LTnz59mnbt2jFkyBBq1qxJ165defvttzl//vwzrvnzRSqspaiEtm3bRrt27XBycsLMzIzZs2cTHh6uPe/j48PIkSPx9PTkyy+/JDg4WHtuwoQJfPHFF7Rr1465c+eWqHupY8eOBAQEcPr0abp168agQYOIiYkpc/0FQXg+/XD9B1JyUvCw9qCHaw+d5Bken87C/beQSzDF1ZkTy69wYV8oqiw1jq4W9JvSnB6jGz81sAEYPHgwFhYWdOrUKV/ruCYzE+WBAwBY9uurkzoLlaPSWm6ys7O5dOkSH3/8sfaYXC7H09OTM2fOFHpN27Zt2bJlC+fPn6dVq1bcu3eP/fv388477xRZTlZWFllZWdrflUplqeopMzamjv+lEqVNv3iR+x98WGw6l++/w6RlyxKVXRLu7u7IZLJSDxo+c+YMQ4cOZf78+Xh5eWFpaYmvry9LlizRppk3bx5Dhgxh3759HDhwgLlz5+Lr60u/fv0YOXIkXl5e7Nu3Dz8/PxYtWsSSJUsYP358kWWamppSu3Ztateuzauvvoq7uzs//PBDvteBIAgvtui0aH69+SuQuzmmXFb+79EajcTU3wOolibRVW1C9vl4ACztjXm1by1qNbcvshtfkiTu3btHrVq5C/zVq1ePkJAQbGxs8qVLOXoUTUoKes5VMHnK8Ajh+VdpLTdxcXGo1WocHR3zHXd0dCTqicG4eYYMGcJnn31G+/bt0dfXp1atWrzxxht88sknRZazaNEiLC0ttT8uLi6lqqdMJkNuYlKiH9N27dBzcoKixsnIZOg5OWHarl2J8ivp2go2NjZ4eXmxZs0a0tLSCpxPSkoq9LrTp09To0YNZs2aRcuWLXF3dycsLKxAOg8PDyZPnoyfnx/9+/dn48aN2nMuLi589NFH7NixgylTprB+/foS1TmPRqPJF3wKgvDi++7qd2Sps2ju0JwOVTuU6BqNRiLidiJ3LkQRcTsRjSZ/i/QPu25R+1oafdMNMcmSMDbX57W3PHh7Xmtqt3Ao8v0yPT2dIUOG0LRpU+7cuaM9/mRgA5C8608ALPv0QSY2xHyhvVD/944fP87ChQtZu3Yt/v7+7Nixg3379vH5558Xec3HH39McnKy9uf+/fsVVj+ZQoHjJ49aIJ78Q3v0u+MnH5doMHFprVmzBrVaTatWrfjjjz8ICgri5s2brFy5kjZt2hR6jbu7O+Hh4fj6+hIcHMzKlSvZuXOn9nxGRgbjxo3j+PHjhIWFcerUKS5cuEC9evUAmDRpEocOHSIkJAR/f3+OHTumPfektLQ0PvnkE86ePUtYWBiXLl3ivffeIyIigoEDB+r8eQiCUDnClGHsCNoB5LbalORLWvDlGDZ/cppdyy5z+IdAdi27zOZPThN8OYbEqDS2r7hMtl8kVdUK0JPlzoD67OkzoPIYGBjw8OFDMjIynrquVk50DGmnTgFg1adPKe5YeB5VWreUnZ0dCoWC6OjofMejo6O1o9efNGfOHN555x1GjhwJQKNGjUhLS+ODDz5g1qxZhW49b2hoiKFhwdHyFcWia1dYsbzgOjeOjhW6zo2bmxv+/v4sWLCAKVOmEBkZib29PS1atGDdunWFXtO7d28mT57MuHHjyMrKomfPnsyZM4d58+YBoFAoiI+PZ9iwYURHR2NnZ0f//v2ZP38+AGq1mrFjx/LgwQMsLCzo1q0by5YtK7QshULBrVu3+Omnn4iLi8PW1pZXXnmFf/75hwYNGlTIMxEE4dlbfXk1aknNa9Veo7lj8dsWBF+O4eB31wscT0vKyj0uAyTQIBFrp8/0Ka0ws3767Ni8cYgymQw9PT22bt1KaGgobdu2LfIa5Z7doNFg3Lw5BjVrFltv4fkmk8ozGrWcWrduTatWrVi1Knf1So1GQ/Xq1Rk3bhwzZ84skL5FixZ4enry1VdfaY9t3bqV999/n5SUlHwj3ouiVCqxtLQkOTkZC4v8O8dmZmYSEhKCq6truaeWS2o16RcvoYqNRc/eHpOWLSqkxUYQykqXr3dBAAiMD2Tw3sHIkPG79+/Usanz1PQajcTmT06TlvT0rum7CjUXrTT4TnuNqlZPH4uYlJTEqFGjaNu2LZMnTy5RvSVJ4p63N9l3g3H6bD7WgwaV6Drh2Xra5/eTKnUquI+PD8OHD6dly5a0atWK5cuXk5aWxrvvvgvAsGHDqFq1KosWLQLA29ubpUuX0qxZM1q3bs3du3eZM2cO3t7eJQpsniWZQoFpazEgTRCE/46V/isB6OHWo9jABiAyKKnYwAbgopGKcX0bFBvYAOzatYvt27dz4MAB3nnnnRItNZF5/QbZd4ORGRpi0b17semF51+lBjeDBw8mNjaWTz/9lKioKJo2bcrBgwe1g4zDw8PzdTXNnj0bmUzG7NmziYiIwN7eHm9vbxYsWFBZtyAIgiAA5yPPc+rhKfRkeoxtOrZE16QpSzaZoKWTBYNalmwyyPDhw7l69Spvv/12idfQylvbxtzTE4V5wc03hRdPpXZLVYZn1S0lCM878XoXdEWSJP63/39cjbvKW3XeYtars0p0XcTtRHYtu1xsug4f1Kdx88LHYiYkJPDNN98wf/587erppaHJzuZuh9dQJyfjsn49Zh3alzoP4dl4YbqlBEEQBN2qjPF+f93/i6txVzHWM+bDJsWv9QWgztFw72rsU9NISChM9WnY1LHQ8xqNhk6dOnHlyhU0Gg1ffvllqeueevw46uRk9BwcMG1b+MxS4cUjghtBEISXhNLPr+BMTSenCp2pqdaoWeWfOynkf/X+h51x8V1B8Q9TOfxjIPEPUrXHJCRkyPL9DjK6DK2LXF74dHK5XM6nn37KjBkzGFTGQcD/rm3TW0z6eIm8UOvcCIIgCIVT+vkRMXFSvsAGcvezi5g4CaWfX4WUu/feXoKTg7E0tOTdhu8+Na0kSVw7/oDfF10k/kEqRmb6OPWsxp8mWaTI8o+QSJFJ/GmSxV0Ddb7jcXFx+Rbj69+/P9evX6d58+KnnT9JFR9P6okTgNgB/GUjWm4EQRBecJJaTfTCRVDYEEpJApmM6IWLMO/cWaetE9nqbNYErAFgZMORmBsUPRg3XZnNX5tvEnY9d9uE6vVteOOdunT99hSRBhqC9LOoppJjKslIk0k80NOADObvCaRLfScUchkBAQH06tULMzMzLl68iJmZGUCZ1zJT7t0LKhVGjRph+GhrBuHlIIIbQRCEF1z6xUsFWmzykSRUUVGkX7yk0yUqfrv9G5FpkTiYOPBW3beKTBd6LY6/Nt8kIyUHhZ6cNv1r0fiNapwNSSAyOTO3ijK4r68pcG1kcibnQxJoU8uWatWqaY9HR0drg5uySsrrkhKbZL50RHAjCILwglPFPn1gbmnTlURaThrfX/0egNFNRmOkV3DGnSpbzek/7nLt7wgAbKua0uW9BthWzQ1KYlIyiy1HUmVr09nZ2XHo0CFq1KhR7sAm89Ytsm7eRKavj2UP3exaLjw/xJib/xiZTMauR2s6CILwctCzt9dpupLYfGMziVmJ1LSoSd/afQucj3uQwm+LLmoDmyadXHhzZkttYANgb/b07qTM8KtEfP8B969f0B5r0KBBuQMbgOSduwAw69gRhZVVufMTni8iuKkgxe1wWxGioqIYP348bm5uGBoa4uLigre3N0ePHq3wskvro48+QiaTsXz58squiiC88ExatkDPsfDp0gDIZOg5OWHSsoVOykvITGDTjU0AjGs2Dj35v50AkkYi4Eg4v395kcTINEwsDPAe34T2g9zR0/93vE9KZg4/nAx5ajlpN46jTolj16Y1Oqm3to45OSTv3QuILqmXleiWqgDBl2P4Z1tQvmXFTa0M6TDYnVrNHCqkzNDQUNq1a4eVlRWLFy+mUaNG5OTkcOjQIcaOHcutW7cqpNyy2LlzJ2fPnsXZ2bmyqyIILwWZQoFZ584k/fpr4QkkCcdPPtbZYOL1V9eTrkqnvm19utTooj2elpTFkU2BPLiVCEDNxnZ0eqcuxuYG+a4PiUtj1OaL3I1JRU8uQ6WR8vbH/PeeABvPD/B+tT7rl36hk3rnSf3nJOr4eBS2tpi1F4v2vYxEy42O5e1w++R+KXk73AZfjqmQcseMGYNMJuP8+fMMGDAADw8PGjRogI+PD2fPni3yuhkzZuDh4YGJiQlubm7MmTOHnJwc7fkrV67QsWNHzM3NsbCwoEWLFly8eBGAsLAwvL29sba2xtTUlAYNGrB///6n1jMiIoLx48fzyy+/lGk1UUEQCpJyckh7NKVZXkiXjVH9+ph36VLgeFk8TH3IttvbAJjYfCJyWe7HyL3LsWz9/BwPbiWipy/n9SF16DG6UYHA5u87sfRZfZK7Mak4Whjyx+i2fPu/5jhZGpERGkDiiZ8BcLI04rt32/Lrd8swNTXVSd3z5G23YNmrFzLxPvRSEi03xZAkCVV2wRH8hdFoJP7Zduepaf7ZFkS1ujZFLkr1OD0DOTJZ8ekSEhI4ePAgCxYsKPRNwOop/cnm5uZs2rQJZ2dnrl27xqhRozA3N2f69OkADB06lGbNmrFu3ToUCgUBAQHaoGTs2LFkZ2dz4sQJTE1NCQwMfGpfuEaj4Z133mHatGk0aNCg2PsSBKFkknbtIufBAxR2dtQ6eIDMG4GoYmORsrOJ/PRTMgMDSTlwAAsdDJxdG7CWHE0OrZ1a06ZKG3Ky1Jz8PYjAkw8BsHMxo+v7DbB2yv9eJEkSG/4JYdGBm2gkaF7dim//1wIHCyOauFhRyyidenO8UKvVzHm3N5Pf74GiBO+TpaVKTCT12DEALPv303n+wvNBBDfFUGVr+H7i3zrLLy0piw2TT5Qo7QcrXkffsPhm5Lt37yJJEnXr1i11fWbPnq39d82aNZk6dSq+vr7a4CY8PJxp06Zp83Z3d9emDw8PZ8CAATRq1AgANze3p5b11Vdfoaenx4QJE0pdT0EQCidlZxO/7lsAbEe+j8LMLN9075yHD4lbvZqoLxZg0qYNetbWZS4rOCmYPff2ALmtNrHhKRz+MZCk6HSQQbMu1Wnd2w2FXv5OgcwcNR/vuMbOy7mDiwe3dOGzvg0w1Pv3/c29di1mzJhBfHw8Y//Xr0ICGwDl/v1IOTkY1quHUZ3idy4XXkwiuHkJlGfv023btrFy5UqCg4NJTU1FpVLl25DMx8eHkSNH8vPPP+Pp6cnAgQOp9WixqwkTJjB69Gj8/Pzw9PRkwIABNG7cuNByLl26xIoVK/D39y9Ra5QgCCWTtGMnOQ8forC3w/qtgmvN2H4wCuXBA2TfDSbm68U4L1pY5rJWXV6FRtLgWc2T7Evm/LH7EhqNhKmVIZ7v1qdanYKBU2RyBh9svsS1iGQUchmf9qrPsDY1kMlk+Pn58corr2D9KOD64osvKvz9IW+7Bau+fSq0HKFyieCmGHoGcj5Y8XqJ0j4MSmLv6ivFpus1rgnO7lYlKrsk3N3dkclkpR40fObMGYYOHcr8+fPx8vLC0tISX19flixZok0zb948hgwZwr59+zhw4ABz587F19eXfv36MXLkSLy8vNi3bx9+fn4sWrSIJUuWMH78+AJl/fPPP8TExFC9enXtMbVazZQpU1i+fDmhoaGlqrsgCLk7Wsd99x0AdqM+QF7I7u5yAwOqfP45YUOGkrxzJ5bevTBt27bUZV2NvcrR8KOYZ9vwysU3ORtyD4Baze15Y2hdjEwLjl25GJrAR1v8iUvNwtpEnzVDm9O2Vu7eU8uWLcPHx4e+ffuyY8cOZDJZhQc2WXfvknntGujpYdGrV4WWJVQuMaC4GDKZDH1DRYl+XOrbYGr19HUbzKwNcalvU6L8SvqHbmNjg5eXF2vWrCEtLa3A+aSkpEKvO336NDVq1GDWrFm0bNkSd3d3wsLCCqTz8PBg8uTJ+Pn50b9/fzZu3Kg95+LiwkcffcSOHTuYMmUK69evL7Ssd955h6tXrxIQEKD9cXZ2Ztq0aRw6dKhE9ykIQn5J27ejioxEz8EBq8FFbxxp0qwZ1kOGABA5dx6ajIxSlSNJEsv9l1MrrhlvX51FYkgWeoYKOg2rh9eohoUGNr7nw3l7/VniUrOo62TO7nHttYENwGuvvYaBgQHVqlVDpVKVqj5llTeQ2Oy119CztX0mZQqVQ7Tc6JBcLqPDYHcOfne9yDTtB7mXaDBxaa1Zs4Z27drRqlUrPvvsMxo3boxKpeLw4cOsW7eOmzdvFrjG3d2d8PBwfH19eeWVV9i3bx87d+7Uns/IyGDatGm8+eabuLq68uDBAy5cuMCAAQMAmDRpEt27d8fDw4PExESOHTtGvXr1Cq2fra0ttk+8mejr6+Pk5EQd0e8tCKWmycoi/rvcFYJtP/wAeTH7K9lPnkzK0aPk3L9P7OrVOE6bVuKyToacxuykBy1ic8fyOLpa4PlufawcTAqkzVFr+HxvIJvP5H5R6tHIiW8GNsHEQI/4+Hjt+0CLFi24desWrq6uJa5HeUhqNcm7c8cLWYouqZeeaLnRsVrNHOj2YcMCLThm1oZ0+7Bhha1z4+bmhr+/Px07dmTKlCk0bNiQLl26cPToUdatW1foNb1792by5MmMGzeOpk2bcvr0aebMmaM9r1AoiI+PZ9iwYXh4eDBo0CC6d+/O/PnzgdxupbFjx1KvXj26deuGh4cHa9eurZD7EwQhv6TffkcVHY2ekxNWAwcWm15hZorT3E8BSNi4iYwbN0pUzsPgRM6tiqVObCskmUTLHjXpN7V5oYFNfGoW/9twThvYTO3qwZohzdFDw5QpU6hTpw7379/Xpn9WgQ1A2ukzqGJiUFhZYf7GG8+sXKFyyKTyjEZ9ASmVSiwtLUlOTs43cBYgMzOTkJAQXF1dMSqk77o0NBqJyKAk0pRZmFoYUsXdqkJabAShrHT5eheeLU1mJsFduqKKjcVp3txCBxIXJcLHB+X+AxjWr4frb78h0yu8AV+j1nDxQBgX9t0DSUaqYSJ9P3yFOvWrF5r+xsNkPth8iYikDMwM9Vg2uCld6ueumpyVlUW7du24dOkSa9asYcyYMaW/6XKK8JmCcv9+rIcOxWnO7OIvEJ47T/v8fpLolqogcrmMqoXMHBAEQSivpG3bUMXGoudcBav+/Ut1reMnn5B66jRZgTdJ+OknbN9/v0Ca5NgMjmy8QdQ9JSDjjt1F6vS2LDKw2Xc1kqm/XyEjR01NWxPWD2uJu6O59ryhoSG//fYb165do0+fZ98lpE5JIeXRNjSWffs+8/KFZ090SwmCILxANBkZxK3fAIDd6NHIDAyKuSI/PTs7HB+tYxW7chXZj00ikCSJ22cj2bbgPFH3lMgMNBypvZmARvsY3ux/BeuikVh86BZjf/UnI0fNax72/Dm2PdWtDBg/fjw//vijNq2bm1ulBDYAygMHkLKyMHSvjVFDsYDof4EIbgRBEF4giVt9UcfFoV+tGlZlbIWw7N8Pk1dfRcrKInLuPCRJIis9h8M/3ODIppvkZKpxrGXOgRZruWt/iQ8af4Cpfv4Vh5WZOYzafJE1x4IB+PA1NzaOeAVLE322bNnC6tWrGT9+PLGxseW95XLL2wHcsm9fsc7Wf4TolhIEQXhBaNLTid/wWKtNGfdFkslkVPlsPve8e5N+9ix3Nuzm7D07UhOzkMlltOrlypVqfxF6OQhnU2cGeuQfsHwvNpVRmy8SHJuGoZ6crwY0pm+zqtrz7777LseOHWPIkCHY29uX/YZ1IDs0lIzLl0Eux8Lbu1LrIjw7IrgRBEF4QST++ivqhAT0q1fHsk/vcuVlUL06tuPGc27HbcIumoIsCwt7Y7q8Vx+TqjIm/vEDAGObjcVA8W/X1/HbMYzfepmUTBVVLI347p0WuNsasnLlSsaNG4dcLkcul7Nly5Zy1U9Xkv7MXZHYtH079B0qZraq8PwRwY0gCMILQJ2aRvyG3IDDbszoImc5lVRSdDrHYhsRWyN3OnZ1RThes/6HgZEeK/1XosxWUtuqNj1dewK543G+O3GPrw7eQpKgRQ1r1v2vOfZmhrRv357Tp0+TkpLCrFmzynejOiRpNCT/mbfdQt/KrYzwTIngRhAE4QWQ+MsvqJOSMKhRA8sSbh1Q2JIUMhncPBXJP7/dQZWtwcBQhkfADzhEXyLrTA2SWzdky83cVpcJzSagkCvIzFEz44+r/BmQu/P3261cmNf7340vR40aRVBQEK+88krF3HwZpZ8/j+phJHJzc8w6d67s6gjPkAhuBEEQnnPq1FTiH808shs3tkStNsGXY/hnWxBpSVnaY6aWBphZGxEdqgSgah0rPEfUJ23DRRJ+uETU/M/4Y+5rZKgyaGLfhDdc3uBhUgYf/HyR6xFK9OQy5vZuQP9GdsRGRVKtWjUARowYQZ8+fbQbYD4v8gYSW/ToUewKzsLLpdSzpQ4ePMjJkye1v69Zs4amTZsyZMgQEhMTdVo5QRAEARI2b0aTnIyBmxsWPXoUmz74cgwHv7ueL7ABSEvOJjpUiUwObfrXos/EZphZG2E/bhz6Li6ooqIw2vAHABObT+RiWCK9V5/keoQSG1MDtoxsTWubLFq3bk3v3r3JzMzU5v28BTbq1DSUfn6A2G7hv6jUwc20adNQKnOj/mvXrjFlyhR69OhBSEgIPj4+Oq+gUPnmzZuHo6MjMpmMXY82nqsIFZ1/SYWGhiKTyQgICADg+PHjyGQy7QakmzZtwsrKqtLqV1JP3ofwYlIrlSRs+gkAu7FjkCkUT02v0Uj8sy3oqWmMzAxo6lkd2aNV0+XGxlSZPw+ALpfUvJnVkKAwR4asP0tcajb1qliwe1w7XnWzxcTEhOjoaCIjI7l37175b7CCpPj5IWVkYFCjBsZNm1Z2dYRnrNTBTUhICPXr1wfgjz/+oFevXixcuJA1a9Zw4MABnVdQKJkRI0Ygk8mQyWQYGBhQu3ZtPvvss3Lvtnvz5k3mz5/Pd999R2RkJN27dy93XefNm0fTCnqzyc7O5uuvv6ZJkyaYmJhgZ2dHu3bt2LhxIzk5OWXKs23btkRGRmJpaanj2hYkAhLhSQk/bUajVGLoXhuLbt2KTR8ZlFSgxeZJGcpsIoOS8h27X9eGY41kyAHPLZHM3X6ZHLVEz8ZV+P3D1lSzzt1Lqlq1auzevZuAgADtZ8HzKG8HcMt+/cTaNv9BpR5zY2BgQHp6OgBHjhxh2LBhANjY2GhbdITK0a1bNzZu3EhWVhb79+9n7Nix6Ovr8/HHH5c6L7VajUwmIzg4d4GuPn36PPdvENnZ2Xh5eXHlyhU+//xz2rVrh4WFBWfPnuWbb76hWbNmZQqqDAwMcHJyKnfdDEq5kqwgqJOTSfgpr9VmbLGtNgBpyqcHNkWlW+m/ksud5LQIlmMTE83Au39RdcJ4Otim0671K2zYsEE7YLhNmzalvJNnK/vBA9LPnweZrNxT5oUXU6lbbtq3b4+Pjw+ff/4558+fp2fP3GmCd+7c0Q4uexmlpaWRlpbG4/uMZmdnk5aWRlZWVqFpNRqN9lhOTg5paWn5+qiflrYsDA0NcXJyokaNGowePRpPT092794N5G5cN3XqVKpWrYqpqSmtW7fm+PHj2mvzulp2795N/fr1MTQ05L333sP70aJXcrk8X3CzYcMG6tWrh5GREXXr1i2wG/iDBw94++23sbGxwdTUlJYtW3Lu3Dk2bdrE/PnzuXLliraladOmTQXupVOnTowbNy7fsdjYWAwMDDj6aI+YJy1fvpwTJ05w9OhRxo4dS9OmTXFzc2PIkCGcO3cOd3d3IHfcWPv27bGyssLW1pZevXppg7jCPNktlWfXrl24u7tjZGSEl5dXvt2O81qnNmzYkG9jyuLKztsluVmzZshkMt54bPfi4p75+fPnadasGUZGRrRs2ZLLly8XeU/CiyF+0yY0qakYenhg3rVria4xtSjZwNnH012Musg/Ef+QaqxgbaPc1tn/Bf3FSBf4+uuvuXr16gs17CBv+rfJq63Rr1KlkmsjVIZSBzerV69GT0+P7du3s27dOqpWzV2V8sCBA3QrQZPpi8rMzAwzMzPi4uK0xxYvXoyZmVmBD2EHBwfMzMwIDw/XHluzZg1mZma8/8QmdTVr1sTMzIybN29qjxX2YV8WxsbGZGdnAzBu3DjOnDmDr68vV69eZeDAgXTr1o2goH/75tPT0/nqq6/YsGEDN27cYOXKlWzcuBGAyMhIIiMjAfjll1/49NNPWbBgATdv3mThwoXMmTOHnx59w0xNTeX1118nIiKC3bt3c+XKFaZPn45Go2Hw4MFMmTKFBg0aaPMcPHhwgbqPHDmSX3/9NV/guGXLFqpWrUqnTp0Kvd9ffvkFT09PmjVrVuCcvr4+pqa5y8enpaXh4+PDxYsXOXr0KHK5nH79+uULMIuTnp7OggUL2Lx5M6dOnSIpKYm3ntiZ+e7du/zxxx/s2LFD281UXNnnz58HcltFIyMj2bFjR4mfea9evahfvz6XLl1i3rx5TJ06tcT3Izx/VImJJG7+GXg0Q0pesrdrCwdj7ViaophZ504Lh9z1a+aeXAxAdtIr3G/YDVmb9shUKiLnfMrqVasYOXKk9rX4vJMkieRdj9a26devkmsjVJZSd0tVr16dvXv3Fji+bNkynVRIKD9Jkjh69CiHDh1i/PjxhIeHs3HjRsLDw3F2dgZg6tSpHDx4kI0bN7Jw4UIgt8Vo7dq1NGnSRJtX3sDZx7tl5s6dy5IlS+j/aDdiV1dXAgMD+e677xg+fDi//vorsbGxXLhwARsbGwBq166tvd7MzAw9Pb2ndvX079+fcePG8eeffzJo0CAgN+jLG1tUmKCgoHwtHUUZMGBAvt9//PFH7O3tCQwMpGHDhsVeD7nPavXq1bRu3RqAn376iXr16nH+/HlatWoF5Lbsbd68Od/y88WVnZfW1ta21M9co9Hwww8/YGRkRIMGDXjw4AGjR48u0f0Iz5+EjZvQpKVhWK8e5p6eJbomXZnNnhUBSBrpqenaD3JHLpeh1khM2vMr4WmBSBp9WlgO5KO6puxLr0avKyZkXL6MxYEDrF+/Xhe39ExkXLpEzv37yE1MSvzchJdPmda5CQ4OZuPGjQQHB7NixQocHBw4cOAA1atXp0GDl3PH1dTUVABMTEy0x6ZNm8akSZPQe2LNiZiYGCC35STP2LFjGTVqFIon+sxDQ0MLpB0xYkSZ6rh3717MzMzIyclBo9EwZMgQ5s2bx/Hjx1Gr1Xh4eORLn5WVha2trfZ3AwMDGjdu/NQy0tLSCA4O5v3332fUqFHa4yqVSjvgNiAggGbNmmkDm7IwMjLinXfe4ccff2TQoEH4+/tz/fp1bTdbYR7vMnyaoKAgPv30U86dO0dcXJy21SQ8PLzEwY2enl6+Bcvq1q2LlZUVN2/e1AY3NWrUKLCvTlnKLskzv3nzJo0bN9Z2f8HzPy5CKJoqMZGER9sX2Jew1SZdmc2uZZdJjErHzNqQ5l41uHQwLN/gYjNrQ9oPcqdWMweUmTlM2HqJ89kbURhBA7OefN7Cgwb165GVlUXN6TOo/+efxC5ZinmnTuiXc9zZs5L0aCCxebduyB97vxb+W0od3Pz99990796ddu3aceLECRYsWICDgwNXrlzhhx9+YPv27RVRz0qX16XxOAMDg0IHiRaWVl9fH/1CNrkrKm1ZdOzYkXXr1mFgYICzs7M26EpNTUWhUHDp0qUCwZWZmZn238bGxsUOGs4L8tavX69ttciTl/fjgVp5jBw5kqZNm/LgwQM2btxIp06dqFGjRpHpPTw8uHXrVrH5ent7U6NGDdavX4+zszMajYaGDRtqu/B0pbD/t2UpuyTPXHi5JPzwA1J6Okb162NWRDfs4zJSs/lz+WUSI9MwtTKkr08zLO1NaPBa1QIrFMvlMoIfbXwZnnUS46rRGCnM+L73VCwNLRk5ciTh4eG8PsWH1NBQMq5cIWr+Z1Rbu+a5n1Sgycgg5cBBAKz69a3cygiVqtTBzcyZM/niiy/w8fHB3Nxce7xTp06sXr1ap5UTSsfU1DRf90+eZs2aoVariYmJoUOHDuUqw9HREWdnZ+7du8fQoUMLTdO4cWM2bNhAQkJCoa03BgYGqNXqYstq1KgRLVu2ZP369fz666/Fvr6GDBnCJ598wuXLlwuMu8nJySE7O5vMzExu377N+vXrtc/i8UUpS0qlUnHx4kVtK83t27dJSkqiXr16RV4THx9fbNl5wfLjz6ckz7xevXr8/PPPZGZmaltvzp49W+r7EiqfKj6ehF9+BcBu/LhiA4rM1Bz+XB5AwsM0TCwN6Ds5N7ABkMtlVK2Tf3G9Y7dimLD1MilZmVi4HyY9LIMPvT7E0jC3FXDZsmXo6ekhk8kw/+Jz7vUfQOqxY6QcOlSiqeiVKeXIETRpaehXq4ZxixaVXR2hEpV6QPG1a9foV8ggLQcHh3yDbYXnh4eHB0OHDmXYsGHs2LGDkJAQzp8/z6JFi9i3b1+p85s/fz6LFi1i5cqV3Llzh2vXrrFx40aWLl0KwNtvv42TkxN9+/bl1KlT3Lt3jz/++IMzZ84AuYOoQ0JCCAgIIC4ursBss8eNHDmSL7/8EkmSCn3dPW7SpEm0a9eOzp07s2bNGq5cucK9e/f47bffePXVVwkKCsLa2hpbW1u+//577t69y19//VWmWSD6+vqMHz+ec+fOcenSJUaMGMGrr76qDXYKU5KyHRwcMDY25uDBg0RHR5OcnAwU/8yHDBmCTCZj1KhRBAYGsn//fr755ptS35dQ+eI3/ICUkYFRo0aYFTOGLDMthz9XXCb+QSrGFrmBjZVjbmCj1kicCY7nz4AIzgTHo1JrWHc8mPd+ukBKlorata6TcDqYe5/d4+zqfwNhfX19bUBl6O6O3aOu0KgvFqB+9Hp8XuVtt2DZp0+JB2ALL6dS/9+3srLSzpp53OXLl7Uzp4Tnz8aNGxk2bBhTpkyhTp069O3blwsXLlC9evVS5zVy5Eg2bNjAxo0badSoEa+//jqbNm3STmM2MDDAz88PBwcHevToQaNGjfjyyy+1XSgDBgygW7dudOzYEXt7e7Zu3VpkWW+//TZ6enq8/fbb+caTFMbQ0JDDhw8zffp0vvvuO1599VVeeeUVVq5cyYQJE2jYsCFyuRxfX18uXbpEw4YNmTx5MosXLy71MzAxMWHGjBkMGTKEdu3aYWZmxrZt2556TUnK1tPTY+XKlXz33Xc4OzvTp0/usvHFPXMzMzP27NnDtWvXaNasGbNmzeKrr74q9X0JlUsVG0vio78H+2JabbLSc9i9IoC4+6kYm+vTd3IzrJ1yu0IPXo+k/Vd/8fb6s0z0DeDt9WdpOO+Qdkfvwa0cyLHww8DRACTISM8osmvU9qMPMXBzQx0XR3QZ/laelZyoKNIefYES2y0IMqmkozAfmTp1KufOneP333/Hw8MDf39/oqOjGTZsGMOGDWPu3LkVVVedUCqVWFpakpycjIWFRb5zmZmZhISE5FuXRKhcoaGh1KpViwsXLtC8efPKrs5LRbzenz/RixaR8NNmjJs0oYbv1iKDm6wMFbtXBBATqsTITJ++Ps2wdc4dP3fweiSjt/hT1Bv7m03sca9/mdUBq3Exd+HTap/S+pXWTw2k0i9dImzo/wCovmkjpq++Wq77rAhx331P7LJlmLRsSY0tP1d2dYQK8LTP7yeVuuVm4cKF1K1bFxcXF1JTU6lfvz6vvfYabdu2Zfbs2WWutCA8Licnh6ioKGbPns2rr74qAhvhpZcTHUPiVl8A7CaMLzLYyM5QsWflo8DGVJ8+k/4NbNQaifl7AgsNbCRJQ/K5Hawa04v1Z3Kndo9vNp5XW71a7LgekxYtsHo7dx2nyE/nonliMdLKlru2zS4ALMVAYoEyBDcGBgasX7+e4OBg9u7dy5YtW7h16xY///yzmLkh6MypU6eoUqUKFy5c4Ntvv63s6ghChYtfvx4pOxvj5s0xbdu20DTZmSr2rLpCdIgSQ1M9ek9qil21f2c8ng9JIDK58MBDUmWTdu0w2UmxPDj2gLo2dfGq6VXi+jn4+KDn6EhOeDhxa9YWf8EzlHn1KtkhIciMjTH3er4HPQvPRqlnS508eZL27dtTvXr1Mo3XEISSeOONN0q8bo0gvOhyoqJIejRmy76IVpucLDV7V18h6l4yhiZ69JnYDHsX83xpYlKKblGR6xth338MUtYabDpaMrH5ROSykn+/VZib4zT3Ux6MGUv8jz9i0aM7Rk+ZHfgsJe3cCYB5F08UZgWXYBD+e0rdctOpUydcXV355JNPCAwMrIg6CYIg/KfEf/89Uk4OJi1bYvLEWkYAOdlq9q25QuTdZAyM9eg9sSn21c0LpHuYmKH9tyRpSD7zG2m3/l1uwKz+HWw7WVHHsintnNuVup7mnTph7uUFajWRs+cgqVSlzkPXNFlZKPcfAMCqb9/KrYzw3Ch1cPPw4UOmTJnC33//TcOGDWnatCmLFy/mwYMHFVG/SiFaDIT/AvE6fz7kPHxI4u+5i58WNtYmN7C5SsSdJAyMFHhPaIJDjScmQ+Somb/nBl8duq09lnr1CEknNhN/YCXqtETkBjHoW10EYFbbqWVekM9p9izkFhZk3rhBwubKH7ibeuwYGqUSvSpVCg0Mhf+mUgc3dnZ2jBs3jlOnThEcHMzAgQP56aefqFmzZpEbGr4o8lYGTk9Pr+SaCELFy3udl3VFbEE34r79DnJyMGndGtMn1klS5ag5sO4qEbcT0TdU4D2hKU6ulvnS3I1Jod/a02w8FQpAx7r2yADzRp0xqtkMm84jkZtYYWh/GJlMooFlG5o7Ni1zffXs7XGcPg2A2JUryb5/v8x56UJel5Rl797IxLhP4ZEy7S2Vx9XVlZkzZ9KkSRPmzJnD33//rat6VQqFQoGVlZV2bygTE5PnfrlxQSgtSZJIT08nJiYGKysrMRGgEmU/iCDp0W7b9uPH5TunylFz4Ntr3L+ZiJ6hgl7jm+Dk9m9gI0kSW8/f57O9N8jM0WBjrKCXeTjzh/fg0I2o3FlTgz5DJpMhN7qPnsU1ZMj4/PVp5a635YABJO/ZS/q5c0TNnYfLDxsq5b1SFRtL2slTuXXqI9a2Ef5V5uDm1KlT/PLLL2zfvp3MzEz69OnDokWLdFm3SpG3E3NegCMILysrK6un7swuVLy4b9eBSoVp2zaYtGypPa7O0XDw++uE30hAz0CO97jGONe20p5PSs9m5h/XOHgjCoD2tW2J+m0enx8+hL1+FuPHj6dLfSfOhyQQk5LJL2G/czMJvGt5427tXu56y2Qyqsyfx73efUg7fZrkP/+slPEuyXv2glqNcdOmGLq5PvPyhedXqYObjz/+GF9fXx4+fEiXLl1YsWIFffr0ybdb9otMJpNRpUoVHBwcyMnJqezqCEKF0NfXFy02lSw7PFy7XYDduPHa42qVhoPrrxN2LR49fTm9xjbB2f3f/aHOBMczeVsAUcpM9BUyZnSry3vtXFmV1p2zp09ibZ2bViGX0aaWLWcenuHmlUvoyfUY03SMzupvULMmduPGEbt0KTGLvsSsQwf0bG11ln9xJEkiOa9LSgwkFp5Q6uDmxIkTTJs2jUGDBmFnZ1cRdXouKBQK8eYvCEKFiVv3LajVmHbogEnz3I1e1WoNfhtuEHo1DoW+nB5jG2s3vsxRa1hxJIg1x+8iSVDT2ogFPWvRrmFNACZMmEC/fv3yLdEhSRIr/FcAMLjOYKqa6XaLHNt3R6Dcv5+sW7eIXvQlVb95dtszZAYGkhUUhMzAAIse3Z9ZucKLodQDik+dOsWYMWNe6sBGEAShImWHhpK8ezfw71gbtVrD4Q03uBcQi0JPTo/RjXCpawNAeHw6g747w+pjuYFND1cDVHvnM3P0O6geTceWyWQF1h47En6EG/E3MNYzZlSjUTq/D5m+PlU+/xzkcpR795L6DMddJu/6EwBzz84oilmKX/jvKVHLze7du+nevTv6+vrsfvQHWZTevXvrpGKCIAgvq7h160Ctxuz11zFu3BiNWsORjYEEX45Friej+0eNqF4/t4vnz4AIZu28TmqWCnMjPRb1b0Rdk3SaTwtAkiSuX79O06ZNC5Sh0qhY6b8SgOENhmNrXDFdRsaNGmIzbBgJmzYROX8+tfbsQW5asQvpSdnZKPfsAUSXlFC4EgU3ffv2JSoqCgcHB/o+5YUkk8lQq9W6qpsgCMJLJ+teSO5AWMBu3Dg0Gokjm25y92IMcoWM7h80okZDW1Iyc5j75w12XI4AoGUNa5a/1ZRq1rnjG3/77Tdq1aqFu3vhA4R3B+8mVBmKlaEVw+sPr9B7sp8wnpTDh8mJiCBmxQqcPvmkQstLPXECdVISCnu7IreqEP7bStQtpdFocHBw0P67qB8R2AiCIDxd3Nq1oNFg1qkThg0a8NdPNwm6EI1cLsNrVENqNrYj4H4SPVeeZMflCOQyGN7YjOQdc0l+GKLNp1u3bkUGNlnqLNYG5O7/NKrRKMwMzApNpytyExOc5s8HIPHnLWRcuVKh5SXlbZLZuzcyvXKtaCK8pEo95mbz5s1kZWUVOJ6dnc3mzZt1UilBEISXUdbduyj37QPAbuwYjv18k9vnopA9CmxqNLZjzbG7vLnuNOEJ6VS1Mua3D9twe/c6jhw5wqhRo0q0srTvLV+i06NxMnVicN3BFX1bAJi1b4dln94gSblbM2RnV0g5qoQEUo/nju0R2y0IRSl1cPPuu++SnJxc4HhKSgrvvvuuTiolCILwMopbuxYkCTNPT874y7l1Jjew6fp+A0zczPnfhnMsPnQblUaiZ+Mq7J/YgZY1bVi5ciXe3t5s2bKl2MXyUrJT2HBtAwBjmozBUGH4LG4NAIeZM1FYW5MVFET8Dz9USBnKvftApcKoQQMMi2i5EoRSt+dJklToH9eDBw+wtLQs5ApBEAQh884dlAcOIiHjdr2h3D4ViUwGXd6rzz0jDdNXnCApPQcTAwVjWphjnnQNS+PmADg4ODx1Modao8Y/xp/Y9FhORpwkKSsJV0tXvGt5P6vbA0DP2hrHTz7h4bRpxK1dh7mXF4ZubjotIzmvS6pfP53mK7xcShzcNGvWDJlMhkwmo3Pnzug91s+pVqsJCQmhW7dupa7AmjVrWLx4MVFRUTRp0oRVq1bR6on9VR6XlJTErFmz2LFjBwkJCdSoUYPly5fTo0ePUpctCILwrMStWYskSYR0mkrotXRkMnh9WF1+ehDDlrPhADSsasHMDvZ4d2yDUqnEzc2NDh06PDXfI2FH+PL8l0SnR+c7/ka1N9CTP/vxKBa9epK8ZzdpJ/4h8tNPqbF5MzJ5qTsJCpV5+w6ZgYGgr49FT/GeLxStxK/8vFlSAQEBeHl5YWb27wA1AwMDatasyYABA0pV+LZt2/Dx8eHbb7+ldevWLF++HC8vL27fvq0dwPy47OxsunTpgoODA9u3b6dq1aqEhYVhZWVVqnIFQRCepcxbt1AeOkRQ7YE80LiCDOr1dWXSmSCCYlIB+OA1N6Z2rYO+Qoa3tzdBQUEF1q150pGwI/gc90Gi4DicTTc20di+MZ41PCvknooik8moMncuwd69ybh4iaTffsf6Ld2M+8lrtTF/43X0rK2fnlj4T5NJJRmd9piffvqJwYMHY2RkVO7CW7duzSuvvMLq1auB3JlYLi4ujB8/npkzZxZI/+2337J48WJu3bpV5p2MlUollpaWJCcnYyEWfhIE4Rm4P248l0Msue/SCWRg2Maer+88IFulwc7MkGltrOjbvhGGhrnjY9LT09HX13/q+5xao8brD68CLTZ5ZMhwNHHk4ICDKOTPfrX1hM2biV64CLmZGW779qLv6Fiu/CSViqA3OqKOi6Pa2jWYd+qko5oKL4rSfH6Xuq1w+PDhOglssrOzuXTpEp6e/36rkMvleHp6cubMmUKv2b17N23atGHs2LE4OjrSsGFDFi5c+NQp6FlZWSiVynw/giAIz0r69esE3DPPDWyAMFdDvggMJ1uloVNdBybUSuSD/p2ZNu3f3bpNTEyK/QLnH+NfZGADICERlR6Ff4y/bm6klKyHDsWocWM0qalEf/FFufNLPXkSdVwcChsbzIrpqhOEEgU3NjY2xMXFAWBtbY2NjU2RPyUVFxeHWq3G8Ylo3tHRkaioqEKvuXfvHtu3b0etVrN//37mzJnDkiVL+OIpfziLFi3C0tJS++Pi4lLiOgqCIJSHJEn8s/oE4dVzv8SdttbwW0ISBnpy5nnX54fhLbGzNEWpVHL58uVCl9koSmx6rE7T6ZpMocjdmkFPj5TDR1D6+ZUrv7ztFix69URWxpZ74b+jRGNuli1bhrm5ufbfxU1FrCh5iwl+//33KBQKWrRoQUREBIsXL2bu3LmFXvPxxx/j4+Oj/V2pVIoARxCECidJEqc2nOMudQG4IIvnlGRCbQczVgxuQoOqVgD06NGDAwcO4OnpmW+iRnHsTex1mq4iGNXxwHbk+8R/+x1Rn3+O6auvlmkfKHVyMqlHjwJgJWZJCSVQor+k4cP/Xbp7xIgROinYzs4OhUJBdHT+ZtXo6GicnJwKvaZKlSro6+vn2627Xr16REVFkZ2djYGBQYFrDA0Ntf3YgiAIz8qFvSFcuZQOgCbuLMdrN2Fo6+rUVgYwpMcETpw4oV0+oywzTZs7NMfWyJb4zPhCz+eNuWnu0LzsN6EDdqNHk3LwENmhocQs/oYqn39W6jyU+/cj5eRgWKcORvXqVUAthZdNqcfc+Pv7c+3aNe3vf/75J3379uWTTz4huxQrUhoYGNCiRQuOPorGIbdl5ujRo7Rp06bQa9q1a8fdu3fRaDTaY3fu3KFKlSqFBjaCIAiV4cK+EC7sCwWg1t0d7KtZnW//14JZXrX4bN6nXL16lZUrV5arjMSsRDSSptBzMnJb12e0mlEpg4kfJzc01AY0Sb//Ttr586XOQ7vdgliRWCihUgc3H374IXfu3AFyx8AMHjwYExMTfv/9d6ZPn16qvHx8fFi/fj0//fQTN2/eZPTo0aSlpWlXOh42bBgff/yxNv3o0aNJSEhg4sSJ3Llzh3379rFw4ULGjh1b2tsQBEGoEKf23OP8ntw9oGoH7yDBMpONcwbQraETJiYmbNu2jblz5/JJOTaXzFRlMvHYRBKzErE3tsfeOH/Xk6OJI0vfWPrMp4EXxeSVV7AanDsdPGrOp2hKMbYo6949Mq9cBYUCS+9eFVVF4SVT6hWe7ty5Q9OmTQH4/fffef311/n11185deoUb731FsuXLy9xXoMHDyY2NpZPP/2UqKgomjZtysGDB7WDjMPDw5E/tviTi4sLhw4dYvLkyTRu3JiqVasyceJEZsyYUdrbEARB0Lnd225x/9hDANzu7aJaxDHCu73PtXP/UKVrVwBatWr11IVKi6ORNMw5NYersVexMLDgR68fcTF30a5QbG9iT3OH5pXeYvMkh6lTSP3rL7LDwohbuw6HyZNKdF3yzl0AmHXogJ6dXcVVUHiplHqdGwsLCy5duoS7uztdunShV69eTJw4kfDwcOrUqUNGRkZF1VUnxDo3giDomkqtYe13ASiuJgFgFneGVte3cLiOBxN378bOzo7r168XmB1aFqsur+L7q9+jJ9fj+y7f84rTK+XO81lRHj5MxPgJoKeH6x/bMapT56npJbWau506o4qOpury5Vh083pGNRWeRxW6zk3Lli354osv+Pnnn/n777/p2bMnACEhITr5wxUEQXiRRCRlMHXhSW1gk2WdSqvrW0BPjxFLltKiRQsmTZqEnQ5aHXYH7+b7q98DMLfN3BcqsAGw6NIF8y5dQKXK3Tn8KWuUAaSdPYsqOhq5pSVmnTo+o1oKL4NSd0stX76coUOHsmvXLmbNmkXt2rUB2L59O23bttV5BQVBEJ4nGo1EZFASacosrsWnsv1ICO1Tc7uATJtZY7vjawCsBgzAonYtzpw5U+YV1R93Meoic0/nLnkxstFI+tbuW+48K4Pj7NmknT1L5rVrJG7Zgs1js3GflNclZdmzB3IxaUQohVJ3SxUlMzMThUKhkz/iiiS6pQRBKA21RuJ8SAIxKZnoR2YS83cUaUkFB8S6dXDi5y1T2XrwAAurVmPq+XPoOzvrpA7hynCG7B9CclYyXWp04ZvXv0Eu081mlJUhcdtvRM2di8zYGLc9ezCoVrVAGnVqKkHtOyBlZlLzt20YN25cCTUVniel+fwu85axly5d4ubNmwDUr1+f5s0rdy0FQRAEXTt4PZL5ewKJTM7EPVtOn/Tc1oO8qdaPc69rS5WHESiAnEYNdRbYJGclM/boWJKzkmlk14iF7Re+0IENgNXAN1Hu2UP6xYtEzZuHy/rvCywOm3LwIFJmJga1amHUqFEl1VR4UZU6uImJiWHw4MH8/fff2t24k5KS6NixI76+vtjbV95qmIIgCLpy8Hoko7f4IwEyCTpl5LZKPx7YSJJEjioLA30j/vnlBiMys+lQqzY9N27USR1y1DlMPj6ZUGUoVUyrsLLTSoz0yr+3X2WTyeU4ffYZIX37knbyJMq9e7H09s6XJimvS6pvn0pbFV94cZU6/B8/fjypqancuHGDhIQEEhISuH79OkqlkgkTJlREHQVBEJ4ptUZi/p5A8vrsq6nkWEjyfIFNRlYqPx75nA2H56ORNKSnQ4q1O6+OGIF+Eausl4YkSXx+9nMuRF3AVN+U1Z1XY2f88kyFNnRzxW7MGACiFyxElZCgPZcdHk7GpUsgl2PZu3dlVVF4gZU6uDl48CBr166l3mNLYNevX581a9Zw4MABnVZOEAShMpwPSSAyOVP7u5WmYMtBYlos18POcCviEvdjcxc2zTaxxXbUKJ3U4cfrP7Lz7k7kMjmLX1uMh7WHTvJ9nti+/x6GHh6ok5KI/vJL7fG8TTJN27ZFX8zCFcqg1N1SGo2m0EHD+vr6+bZFEARBeFHFpOQGNjIJGmUreD2j4Hues40r/3tjOrbmTtRwyN0c07ZDC/QdHcpdvl+oH8v9lwMws9VMOlTrUO48n0cyfX2qfPE5oYPfQrl7DxY9eyEzNCDx118BsBCtNkIZlbrlplOnTkycOJGHDx9qj0VERDB58mQ6d+6s08oJgiBUBgdzI5xVcv6XaohXhgFGyEjJUvLTX4uIU/773teidkdqOtYDScIwK5E6YwaVu+xrsdf45GTu1gxD6w3l7bpvlzvP55lx48bYDHsHgAejR3N/xLuok5IAiF2yBKWfXyXWTnhRlTq4Wb16NUqlkpo1a1KrVi1q1aqFq6srSqWSVatWVUQdBUEQnpm0pCxS/o5iaKohTmo5mUgcNc5m+ellXAg6wuZjX5FvBY1H/27uEo9BOVttIlMjGf/XeLLUWXSo2oFpLaeVK78XhVHDhrn/eKL1XxUTQ8TESSLAEUqt1N1SLi4u+Pv7c+TIEW7dugVAvXr18PR8PjZoEwRBKAt1joYrf93nwv5QVFlqJCSuGqg5aZRDuhz0Oo/AIjmKHu1G55u9Y5iViEf4bhovXFau8lOzUxn711jiM+Nxt3Zn8euLn7v9oSqCpFYTs2RpESclkMmIXrgI886dkSle/uch6EaZ1rmRyWR06dKFLl266Lo+giAIz1zotThO/hZEcmzu3ngppnJ+y4wiJfEuNvVeJV2ZhZ6FA1bDl3DJ0phOzavTwMYM5ZIFmFw5it3I99CztS1z+SqNimknphGUGISdsR1rOq3BVN9UV7f3XEu/eAlVVFTRCSQJVVQU6RcvYdq67BuOCv8tZQpujh49yrJly7SL+NWrV49JkyaJ1htBEF4oSdHp/PNbEOE34gEwsTAgyd2U1ZevEr1lKrIsJb+eOo3KuiYxKZk4mBvRytUGhVxGytGjqK8cQW5igs3775erHosvLOZkxEmMFEas6rSKKmZVdHF7LwRVbKxO0wkClCG4Wbt2LRMnTuTNN99k4sSJAJw9e5YePXqwbNkyxo4dq/NKCoIg6FJ2hoqL+0O58td9NGoJuUJGk84uKF2Nmb8tAIW5Lc1bNCfpYSiGBvq8Uit/q4yk0RC7ajUA1v/7H3rW1mWuy683f+XXW7mzgxZ1WERDu4Zlv7EXkF4JF34taTpBgDLsLVWtWjVmzpzJuHHj8h1fs2YNCxcuJCIiQqcV1DWxt5Qg/HdJGonb56M4syOYdGU2ADUa2dL+TXfuJsYw4pdAUnMkRrStyaTXqqJQKDA3Ny+Qj9LPj4gJE5GbmlLryOEyBzcnHpxg/F/j0UgaJjWfxPuNytcC9CKS1GrudvZEFR2tHZydj0yGnqMjtY8eEWNu/uNK8/ld6tlSSUlJdOvWrcDxrl27kpycXNrsBEEQnomYMCV/LL7E0U03SVdmY+lgTM+xjek1tglnr56lXeuW3D+6mZY1rPmkRz2srKwKDWwkjYa4vFabYe+UObC5nXCbaX9PQyNp6Fe7H+81fK9c9/eikikUOH7y8aNfnlgs8dHvjp98LAIboVRKHdz07t2bnTt3Fjj+559/0qtXL51UShAEQVfSldn89fNNfv/yItEhSvQNFbTpV4u357SmZiM7JEli6e5zZCvjyQ4+x9IB9THQK/qtMcXPj6ygIORmZtiOGFGmOsVlxDHur3Gkq9Jp5dSKOa/O+U/vn2TRtStVVyxH74nViPUcHam6YjkWXbtWUs2EF1Wpx9zUr1+fBQsWcPz4cdq0aQPkjrk5deoUU6ZMYeXKldq0Yq8pQRAqi1qt4frxCM7vuUd2phqAOq2daNOvFqZWhtp0P5wM4Y5pYxx6+bD9q0lUd7AqMk9JrSZ2dW6rjc3w4SgsLUtdrwxVBuOPjicqLYqaFjVZ+sZS9BUFV0D+r7Ho2hXzzp1zZ0/FxqJnb49JyxaixUYok1KPuXF1dS1ZxjIZ9+7dK1OlKpIYcyMIL7/7gQn889sdEqPSAbCvbk6HwR5UqZUbjBw9epRFixYxa/lGRv56DbVGYp53fUa0e/r7W/LefTycOhW5hQW1jxxGUcr3EI2kYerfUzkcdhhLQ0t+7fEr1S2ql+0mBeE/pjSf36VuuQkJCSlzxQRBECpScmwGp7YHEXIlDgAjM33a9K1F3bZVkMtzu30yMzMZPnw4ERER3Bj/CYat36JvU2eGt6351LwltZq4NWsAsH13RKkDG4BVl1dxOOwwenI9VnRcIQIbQaggZVrnRhAE4XmSk6XG/1AYl/3CUas0yOQyGr9RjVd61cTQJH+Xj5GRET9s2sSH81ahad6PelUsWNS/cbFjXpT79pEdEoLC0hLrd94pdR13Bu1kw7UNAHzW9jNaOLYodR6CIJSMCG4EQXhhSZLE3UsxnP7jLqmJWQBUq2tNh0Ee2Dj/u8LvwYMHsbCwoG3btgCcSHGE9h9gZaTHt/9rjrHB08d1SCoVcWvWAmDz3nsozMxKVc8LURf47MxnAHzY+EO8a3mX6npBEEpHBDeCILyQ4h6k8M+2IB4GJQFgbmtE+zfdcW1ql68V5vfff2fQoEFUq1aNy5cv81doBr+cC0ch0/BTx2xqPDwASkeo0Rae2MtJUqtJv3gJ5f79ZIeFIbe0xHro0FLVMzQ5lEnHJqGSVHSr2Y2xTcVCp4JQ0URwIwjCCyUzNYdze+5x40QEkgR6+nKad6tBsy7V0SukBaZbt254eHjg6elJaJKa2buu4yU/zzdmv2J+LObfhBbO0O0rqN8byF2oL3rhovz7HqnVpJ0+VeKpyUmZSYw9OhZltpLG9o35vN3n/+kp34LwrJRotlT//v3ZtGkTFhYWbN68mcGDB2NoaFjcZc8lMVtKEF5MGo1E4D8RnN19j6w0FQC1WzjQdkBtzG2M8qUNDAykfv362t+VSiVqhRG9Vp2kofJvvjVYDkD+MOPRb4M2o3xgRMTESQVXzJXl/qcka69kq7MZ5TcK/xh/qppVZUuPLdgZ25X2tgVBeKQ0n98lCm4MDAwICwujSpUqKBQKIiMjcXBw0FmFnyUR3AjCi+dhUCIntgUR/yAVANuqpnQY5EHVOvlXB5YkienTp/PNN9+wY8cO+vXrB4BaIzFi43lOBcVw1ngi9lI8hbefyJDMqnB3j2PudgCFJil+OwBJkph9aja7g3djpm/Gz91/prZ17TLevSAIUAFTwevWrcvHH39Mx44dkSSJ3377rciMhw0bVvoaC4IgFCIlIZMzO+4SdDG3+8jQRI/Wvd1o0MEZuaLgKsKPd/n4+/trg5ulh2/zT1Acr+nfxkGKf0qJEunBcaiKiGtyk0iooqJIv3gJ09atCk2y4doGdgfvRiFTsOT1JSKwEYRnrEQtN6dPn8bHx4fg4GASEhIwNzcvtN9YJpORkJBQIRXVFdFyIwjPP1WOmoDD4Vw6GIYqW4NMBg06VKV1bzeMzAqu5qtWq1E8akXJycnh77//xtPTE4BD1yNZ/stOOsovM9rqHOZpYU8tOznMmIdnit8vyvmbb7Ds1bPA8YOhB5n29zQAZreezeC6g4vNSxCE4um85aZt27acPXsWALlczp07d17YbilBEJ5fkiQRciWOU9uDUMZlAlCltiUdBntg71JwE8usrCymT59OVFQUvr6+yGQy9PX18XytDdzaT/LVvfy/vbuOr6p+Azj+ubHddbGG0d09OmSIhWCBRSiolKCgUlLSCAgoAmKASirxA0FCujvdBqNryVjH3b33/P64cPWyYMMFjOf9evmSe873nPOcL7D78M06f2/iT93d1prkB8egtTPmKlatl1emY6eiTzFyz0gAulXvJomNEEXkoVYo9sriL7UQQuSGyaQQHhZHckI6ji46/Cq5oVariA1PZu/K81wPuQOAo5uO5q9UpGJD72xnGAUHB/Ptt99iMBj46N2uNHWLgbDNcHkPGNNxBVxVkI4Om0ptUFdqD7u/hKQoIKtGaxUqlwcM+r075sahofUifDeTbjJw+0D0Jj1tSrVhSIMhea8cIUS+yHNyU6ZMGeLi4vjhhx8ICQkBzJtp9urVC9eH2EROCPHkuHgiij0rwkiOS7ccc3S1xbO0M9f/jsVkUlBrVdRrX5oGz5TFRpfD4nrGDOq5J/N13yBK6S/S9MA7VqdjtL5sSKvFcV1jRvZ/D2+Pu11NTj6wsjvmqU//TnBUpCdouLHXEUsTj0plPWPqbpLlM2K41WDiRH0iA7YNIDYtlqoeVZnaaioatWz4KERRyfPGmUePHqVDhw7Y29vTuLF5MN2RI0dITU1ly5Yt1K9fv0ACzS8y5kaIonHxRBSbFpzNsUy5Op40f7Uirl4OWZ5Pu32D0UP68lFTe/xjD0J6/D8nVRoo3QQqPc1viTX4dFc6Nho1y99vQoMyHtY3Cl4Hm4ZCwi3LoQytP1e3uJMRdQe7mjXx6NGdqBkzrda50fr64jNiuNU0cIPJwIBtA9h3ax9e9l4sfX4pvo6+eagZIURu5PtU8H9r2bIlFStWZOHChWi15oYfg8FA7969uXTpErt37374yAuBJDdCFD6TSeHnEfutWmzuZ+dkwzvTWlg2uATMrSYRp+H8FgjbTPevd/PL6QzaltWwrbsDKkdPqNgeKj8NFZ4Ce3f2X4zh7e8PYVLgi0416N60bDZBGeHqfkiKxKhy5urIeaSfO4dNmdKUXboUbYkSlhWKDdHRaL28cGjYwKrFRlEUJh6ayIpzK7DX2vPTMz9Ro0SNfKo1IcS/Feiu4EePHrVKbAC0Wi2fffYZDRs2zHu0QojizWQkfM9ekuNyHqiblpRBeFgcJcvawKWd5rEzYVshMdxS5vNWtuy7pWHoB6+hemsAlGxgtWXCrbhUPlx6ApMCL9crSbcmZbJ/oFoD5Vpi0uu50fs90s+dQ+PpSenvv0dbogQAKo0m2+neAEtClrDi3ApUqJjccrIkNkI8IvKc3Li4uHDt2jWqVq1qdfz69es4O2eezSCEeILd7f5JjiwPPHiAbfKGyZD8Exj1AKRkKJyMsaFZmw5Q+WkqV3qac195W/3j6p50g5G+S45zO1lPdT8XJr5U64FbHShGI7c+G0rK4cOoHR0p/d0CbAMCcvVqu67vYtqRaQAMbjCYdqXb5eo6IUTBy7wK1gN07dqVXr16sWLFCq5fv87169dZvnw5vXv35o033iiIGIUQj6PgdbCyO0r8LWIyyubqEsfovebExr0sERXfIvD3Ejy9JJ3QeqOhQU9w8c8ysQEYtz6YU9fjcLW3YUG3Bg/e6VtRiJw0mcRNm8DGhlJfz8HuX1s25ORc7Dk+3f0pCgqvVHqFHjV65Oo6IUThyHPLzfTp01GpVHTv3h2Dwby/i42NDX379mXKlCn5HqAQ4jFkMsKmoehNduxM+ICwtNZ3Tyjcv6PT3Qtwsk3C77nXoeoz4FkJL5MJ75+eJjo+lZiYmBwft/LIdZYeuoZKBbNfr0uAR9YDkv/t9ncLubNkCQD+Uybj2KxZrl4tKiWK/tv6k2pIJdAvkJFNRspmmEI8YvI8oPielJQULl68CECFChVwcHjwD5NHgQwoFqIQXN5D5PcfsSVuCAlGX1QYqWi3l7C0VpgTnH83GpsTnmc+qIlfFSfs7OxQq83nI+7OVPL1zX720ekbcbw6/wB6g4nB7SszsF2lB4YXt3oN4SNGAOZp3R653DYmJSOFdza/Q/DtYMq5luPX537FxVZ+jghRGAp0QPE9Dg4O1KpV62EvF0IUU4pJ4cTueA7dnowJLc7qKNq7zcTP9hwV0g6yJ6EXyaZ/FspzcrejRZdKpGgjadiwDT179uSzzz4Dck5qAGKT9fT99Th6g4mgat4MaPvgPZwSd+4kfNQoAEr07pXrxMakmBi+ZzjBt4Nx17kzt91cSWyEeEQ9dHIjhBD3S0nQs21RMNeCzS25FXT7aes6F506xfzZ7iDldIcJ11cj2eSO43Of4deyGWq1ioUL/0dISAhz587lww8/xN7ePsdnGU0KA5ed4GZcKmVLODCjS13raeRZSD15kpsffQxGI66dOuE1JPerCM86Povt17djo7Zh9lOzCXDO3cBjIUThk+RGCJEvrgXf5q+fgklNzEBro6KFwzyq223m/uEoapWJkrpgcPGHli3gbkLSu3dv4uPj6dGjxwMTG4DpW86x90IM9jYaFnRriKt95g01/y390iWuf9AHJS0Nx5Yt8ZswPtdjZVadX8VPZ38CYHzz8dTzrper64QQRSPPs6WEEOLfjAYT+1ddYP2cU6QmZuDhY8NrpSZRwz5zYmNmPniq7Ae8+XY39HrztG+VSsUnn3ySq73rNp2NYN5O85i/qa/WpopvzstQZERGcb33exjj47GrVYtSs75CZZNzMnTPwfCDTDg4AYB+dfrxfPnMO4ELIR4t0nIjhHho8dEpbPn+b6KuJgJQs7EDzW+/hzb5GriXg2Yfwp7pVtsc4OJP+lPjee75gdy6dYvKlSszduzYXD/zQlQSn/x2CoBeLcrxYh3/HMsbExK4/t57ZNy6hW2ZMgQsmI/a0TFXz7oUf4nBOwZjUAw8V+45+tTpk+s4hRBF56GSm7CwMHbs2EFUVBQmk8nq3OjRo/MlMCHEo+384Qh2Lj1HRpoRnYOWp17QUf7I65AaC17VoPtacPY1r09zd5sDnHygTDN0ag3z5tmwcOFCPvzww1w/MyndQJ9fj5GUbqBxOQ+GPVs1x/Km9HRu9B9A+vnzaLw8Cfjhe7QeHtmWN5qMHI86TnRKNHZaO6YdnkZiRiJ1veryRfMvZMq3EI+JPE8FX7hwIX379sXT0xNfX1+rv+wqlYrjx4/ne5D5SaaCC/Hf6NMM7Fl+ntCD5mnafhVdaf90Gs5/dAV9IvjXh7dXgYN1EnHs2DG0Wi116tSxHFMUJdcJg6Io9F96nI1nIvBx0fHHhy3xctZlX95o5ObHg0ncsgW1oyNlfv0Fu2rVsi3/19W/mHJ4CpEpkVbHPew8WNNpDR522SdFQoiCV6BTwSdMmMDEiRMZOnToQwcohHg8RV9LZMsPfxMXmYJKBQ2fL0fDShdR//Y2GFKhTAt4cznorMfAbNiwgZdffpnSpUtz7Ngxyw+mvLSEfLf7EhvPRGCjUfHtWw1yTmwUhciJE0ncsgWVjQ2l5s59YGIzeOdgFDL/Wy82LZbjkccJKhOU61iFEEUrz8nNnTt3eO211woiFiHEI0pRFE5vv8H+NRcwGRSc3HW0f7c6/vpdsKI3mDKgUgfoshhsMs90atq0KT4+PtSsWTNTV3Zu7L8Qw9RNoQCM7liDBmXccyx/e8EC7ixdBioV/tOm4tgkMNuyRpORKYenZJnYAKhQMfXwVNoGtEWjznlLByHEoyHPs6Vee+01tmzZUhCxCCEeQamJejZ8e5q9v4VhMiiUq+NJ15GN8U/aAL+/Y05sarwMXX+1SmzCw//ZzdvDw4ODBw+yevVq3Nzc8vT8W3GpDFhm3un7lfqleDuwdI7l437/nehZswHwGTECl2efzbH87hu7M3VF/ZuCQkRKBMejHu0udyHEP/LcclOxYkVGjRrFwYMHqVWrFjb3TaccOHBgvgUnhChaN0Jj2fpTMCnxejRaNc1frUjN1iVRHZoPm4aZC9XvDi/MgrutGoqi8NVXXzFixAj++OMPgoLM3Tn+/jnPaspKWoaRvr8eIzZZTw1/Fya+VDPHrqzE7TsIHz0GgBLvv49Ht7cz39OQxomoExwMP8ih8EP8ffvvXMUSnRKd5/iFEEUjz8nNd999h5OTE7t27WLXrl1W51QqlSQ3QhQDRqOJI+svc2zzVVDA3deBp3vXxLOkI+z+EnZMNBdsOgCengD3TSwIDQ0lPT2dNWvWWJKbhzFu/d+cuhGPm4MN899ugJ1N9t1CKSdOcHPwYDCZcH3pJbw+/sj8LiYjwbeDORRxiIO3DnIi6gR6kz7PsXg5PHj9HSHEoyHPyc3ly5cLIg4hxCMiISaVrT/+TcSlBACqt/CnxWuVsLFVw5bP4cA35oJtR0KrTy2Jzb9nPs2ePZvWrVvz5ptvPnQcK45cY9nh66hUMOf1ejnu9J1+8SI3+vQ1rz7cqhWpn/Rk+bnlHLx1kCORR0jUJ1qV93bwpolfE5r4NaGhb0O6bexGVEpUluNuVKjwcfChvnf9h34XIUTh+k+L+N2bRS5rPwhReP69FouXgxf1vevn20DXC8ei2PFrKPpUA7b2Wtq8VYVKDX3AZIT1g+D4YnPBZ6ZAk74AmEwmvvzyS65evcq3334LgL29PW+99dZDx3H6Rhyj/mfuLhrSvjKtKmffapIRGcmVXu9iio8nurw7g1uf58Yfr1iVcbZxprFfYwL9Amni14SyLmWtfm4NazyMwTsHo0JlleCo7q6mPLTxUBlMLMRjJM/r3AD8/PPPfPnll4SFhQFQuXJlPv30U7p165bvAeY3WedGPM6yWovFx8GHYY2H/aepyhl6I3tXhhG817ySsE85F57uVQMXT3swZsDq9+Hv1aBSQ8c5UP+fv+vHjh2jUaNGKIrCnj17aNGixcO/IHA7KZ2OX+/lVnwa7av7sODtBpk2xEzUJ3Ik4gjHL+ymwbg1+EbquekBo7tpSHRQYau2pZ53PZr4NyHQN5DqJao/MDnJqm59HXwZ2nioTAMX4hFQoOvczJw5k1GjRjFgwACaN28OwN69e+nTpw8xMTF8/PHHDxe1ECJH2a3FEpUSxeCdg5nZZuZDfQnfvpnE5oVnuRORAipo0KEMjTqWQ6NRQ0YqrOwBYZtBbQOvLIQaL1ld36BBAyZOnIi3t7flZ8LDMhhNDFx+glvxaZT3dGRGlzqo1Sr0Rj0no05aBgGfvX0Wjd7IyBVGfCMh1gl+61ONLjVaEegXSD3vethp7fL07KAyQbQNaFtgrWJCiMKT55abcuXKMW7cOLp37251fPHixYwdO/aRH5MjLTficWQ0GemwqkO2U5bvjQvZ9MqmXH8ZK4rC2V032ff7BYwGEw6utgS9U52AqndX4k1LgGVvwNW9oLU3T/WuFITRaGT27Nn06NGDEiVK5Cr23CYMU/4MZf6uizjYqpjxthfh+jOWQcBpxrR/3tek8PkfdtT6OxmToz0+i77Ds1bDXL23EOLxVKAtN+Hh4TRr1izT8WbNmlmtayGEyD/Ho47nai2WobuHUtmjMi62Lub/dC6Zfq1Va0lLzmDHL6FcOmme3lymZgna9aiGvbOt+YYpsfDrK3DrONg6w1sroYz5732/fv347rvv2LFjB+vWrctxzF1uu9EURWHJsWP8cOp/2JW8iJP7FT47YD0I2NPe0zxmxjeQWosOov97HSobG8rOm4+jJDZCiH95qHVuVq5cyYgRI6yOr1ixgkqVKj1UEHPnzuXLL78kIiKCOnXq8PXXX9O4ceMHXrd8+XLeeOMNOnXqxNq1ax/q2UI8DnK7xsrmq5vZfHVzjmXKpdSgVejr2Ke7YFIZuV0nhMiaMYSG3k2ATOB66Dtc4q7j4uKJS8dvcPGphrPJgFatpW/fvvz++++8+uqrD0xscupGG9tsLDqNjkPhh9h74wDRaRHY+ZnLpBrB0caRRj6NLONmKrhVQKVSEf3tt8SsWmdeffjLL3HMxc8KIcSTJc/dUqtWraJr164EBQVZ+tf37dvHtm3bWLlyJS+99NID7mBtxYoVdO/enfnz5xMYGMisWbP47bffOHfuHN7e3tled+XKFVq0aEH58uXx8PDIdXIj3VLicXQk4gjvbn73geU6lO2Ao40jCekJJOjv/nf318n6ZOrf6ECDGx1QoybOLoq/Ki0mxulGjvdUTArp4enYlbTD0cYRF1sXHIwOeLh54Grrat06dLeFyMnGic/3fU5sWmyu31FRNDgqFehRN4hmJZtS07MmWrX1v7/urFxJxN1F+nxGj8LjP0w1F0I8XvLy/f1Qs6WOHTvGV199RUhICADVqlVjyJAh1KtXL8/BBgYG0qhRI775xrx2hslkIiAggA8//JBhw4ZleY3RaKRVq1a8++677Nmzh7i4OEluRLGWZkijxfIWpBvTszz/oDE3ibFpbP3xb8IvxAPgU0+Hd3uFZFXiP4lQ3BXiQ9aSYEwjwdaBhBLliIlL4PiXx0m+mkzFLypi62mbr+8V4ByAKakGYVf98dRWYcOH7fB0ynpDzMRt27jx4UAwmSjRtw/egwblayxCiEdbgY65AfPsiF9//fWhgvs3vV7PsWPHGD58uOWYWq0mKCiIAwcOZHvdF198gbe3N7169WLPnj3/OQ4hHmUGk4GRe0fmmNhA9muxXDoZzfZfQkhPNmCj09D6zSpUCfS1LnTrJGyaDCm3wasqdFkDLv7o9Xpazm9JsDqYCZUm0LhNY+LT461ahDL9Wp/A9cTrRCRHPPDdqupeY/Vhb2w0KuZ90DTbxCbl+HFuDh5iXn341VfwkpXQhRA5yFVyk5CQYMmSEhISciybl9aQmJgYjEYjPj4+Vsd9fHwIDQ3N8pq9e/fyww8/cPLkyVw9Iz09nfT0f74UHhS/EI8Sg8nAsD3D2HJ1C1q1lp7Ve7L+0vpMA3SzWovFkGFk/+8XOLPrJgDeZZxp36sGbt73rfR77SAseQ3SE8CvLobXV6Jx9kYF2NrasnLlStLT06lcuXKu485tN9q6Y4mAN2NfrEH90lnv9J0eFsb1vv1Q0tNxatsWv7FjZeFQIUSOcpXcuLu7Ex4ejre3N25ubln+YLm39LrRaMz3IO9JTEykW7duLFy4EE9Pz1xdM3nyZMaNG1dgMQlRUAwmAyP2jGDzlc1o1Vq+avMVbQLaMKDegAdOrY4NT2bL92e5fTMZgLrtS9OkU3k0WrX1Qy5sgxVvQ0YKlGnO9RbTeeP5V+jWrRsffPABAGXKlMlz7PW96+Pj4JPtlgaKAorBFUNKOZqWL8GbjbPe6TsjPJxr772PKT4e+7p1KTlzBirtf1pYXQjxBMjVT4nt27fj4WFe+2LHjh359nBPT080Gg2RkdZTXCMjI/H19c1U/uLFi1y5coWOHTtajplMJgC0Wi3nzp2jQoUKVtcMHz6cwYMHWz4nJCQQEBCQb+8gREEwmAyM2DuCP6/8iVatZWbrmbQJaAOARq2hkW8jAEwmhfCwOJIT0nF00eFb0ZVzByLYs+I8hgwT9s42tOtZnTI1sliPJngdrOoFRj1UDIIuv7B63kL27dtHWFgY3bp1w8Eh+/2ccqJRa7Ld0uDeKL/0yI6AmoOXbrP57wieqelndQ9jfDzX3nsPQ0QEthUqUGret6jt7R8qHiHEkyVXyU3r1q0tvy5XrhwBAQGZWm8UReH69et5eritrS0NGjRg27ZtdO7cGTAnK9u2bWPAgAGZyletWpUzZ85YHfv8889JTExk9uzZWSYtOp0OnS7rfnwhHkVGk5GRe0fy5+U/0aq0zGg9g7al22Yqd/FEFHtWhJEc90+3q8ZGjTHDnPAHVHOnXc/qOLpm8ef/5FL4X39QTFC9M7y8ELS2fPjhh9y6dYv333//oRObe4LKBDGzzcxM69woBlfSIztiSKxpOTZufTDtq/uiubvNgiktjev9+qO/cBGttzelF36H1j3rbishhLhfntt3y5UrZ+mi+rfY2FjKlSuX526pwYMH06NHDxo2bEjjxo2ZNWsWycnJvPPOOwB0796dkiVLMnnyZOzs7KhZs6bV9W5ubgCZjgvxODKajIzcN5KNlzeiVWmZ3mY6T5V+KlO5iyei2LTgbObr7yY2lRv7ENSzOip1FmNTDn0Hf34KwNWSnZi1343pr2jQYB7QP3Xq1Hx7n6AyQWjTatBrxW+otIkoBmeMKeWAf7rHFCA8Po3Dl2NpWqEEisHAzSGfkHrsGGpnZwIWLsTG3z/fYhJCFH95Tm7uja25X1JSEnZ2edvLBaBr165ER0czevRoIiIiqFu3Lps2bbIMMr527RpqtfoBdxHi8Wc0Gfl83+dsuLTBnNi0nk670u0ylTOZFPasCMvxXrfC4lAAq7+pigJ7psP2CQDoG3xAy/7LuX79Op5e3owcOTLf3iU2Wc+O0Cj+Colke0gkRmOFB14TlZiGoihEfDGepG3bUNnaEvDtXOyq5H4gsxBCQB6Sm3vjVlQqFaNGjbJqsjYajRw6dIi6des+VBADBgzIshsKYOfOnTleu2jRood6phCPEqPJyKh9o/jj0h9oVBqmtZ5GuzKZExvAPMYmLutp4fck3UknPCyOklXuduUoCmwdDfvnmD+3HoZtm2FMTqjDnDlzeOutt/7zO1yMTuKv4Ej+Conk2NU7mPK4gpa3sx0x38wlbuVKUKvxnzEdh0aN/nNcQognT66TmxMnTgDmlpszZ85ga/vPYl62trbUqVOHTz75JP8jFKKYM5qMjN4/mvWX1psTm1bTaF+mfbblH5TYWMol3C1nMsKGIXDsJy7dMWFsPphKbc1rS7311lt07doV7UPMQDIYTRy9eodtIZH8FRLF5Zhkq/PV/FxoX82btlW96fvrcSIT0rKYN2VuXfJ1taPy4a1EzZ0LgO/oUbi0z74OhBAiJ7n+iXZvltQ777zD7NmzZXVfIfKBSTExZv8Y1l1ch0alYWqrqTxd9ulsy8dFpXBs89Vc3dvRRQfGDFjbF878xo7LRjqvMlF251oOdhyG/d2ZR3lJbBLSMth9Ppq/giPZcS6a+NQMyzkbjYom5UvQvroPT1X1ppT7P627Y1+sTt9fj6MCqwTnXrfZFJ9YosaPB8CzXz/cX3891zEJIcT98vzPtVmzZmEwGDIdj42NRavVStIjRC7dS2z+d/F/aFQaprSaQoeyHbIsq5gUzuy6wYHVFzHcHTScEyd3HX5l7WBFNzj/J6i1VO05A7tNI3FyciIhIcGS3DzI9dgUS+vMocu3yTD+k564O9jQtqo3QdV8aFnJE2c7myzv8UxNP+a9XZ9x64MJj0+zHPd1tWNSBQM+EyagmEy4vfYanh9m3UUthBC5lefk5vXXX6djx47069fP6vjKlStZt24dGzduzLfghCiuTIqJsfvHsvbCWtQqNVNaTuGZss9kWTY+OpXtP4dwKywOgJJV3KlY34tdy85ne/8WL5VCvew1ks7vxsnBHrr8jF/lDuza1YIKFSpgY5N1EgLmAcunbsSxLcQ8IDg0ItHqfHkvR9pX86FdNR/ql3ZDq8ndgP9navoRVMWLE3/sIPFWBM7+vlSv4s+Nnj0x6fU4PfUUvmNGy+rDQoj/LM8bZ3p4eLBv3z6qVatmdTw0NJTmzZtz+/btfA0wv8nGmaKomRQTXxz4glVhqyyJzbPlns1UTjEpnN19k/1rLmJIN6LVaWj2UgVqtiqJSq3Kcp0bJ3cdLTr5UeHMeyzbdIABf6axYck8mrz0QY4xpeqN7L0QY2mhiUn6555qFTQs63E3ofGmvJfTQ713wpYtRE6ajCHiX3tOqdVgMmFfvz6lf/wB9UPMuBRCPBkKdOPM9PT0LLulMjIySE1NzevthHii3J/YTGoxKcvEJiEmle2/hHDzXBwA/pXceKp7NVy9/ulKqlDPm3J1vKxWKPbzTUe95GWUyLP874KK2FSFb1btyTK5iUpIY1toFH8FR7L3Qgzphn+6u5x0WlpX9iKoujdtKnvj7vjfdgNP2LKFm4M++md5YkuFmJ/p9tprktgIIfJNnpObxo0b89133/H1119bHZ8/fz4NGjTIt8CEKG5MionxB8dbEpuJLSbyfPnnrcooisLfe26xf9UFMtKNaG3VNH2pIrVal8y0IJ/RpHD4cixRqWl4e9hRwT0B9eLOEHsJlbMf3636hYZrd/PRRx9Z7h0akWiZrn3qRrzV/Uq62dO+url1JrBcCWzv34fqISlGI5GTJmdObP4levZsXF/siEqTeVdzIYTIqzwnNxMmTCAoKIhTp07Rrp15HY5t27Zx5MgRtmzZku8BClEcmBQTEw9O5Pfzv6NCxYTmE3ih/AtWZRJup7Ljl1BuhN4BwK+iK+16VMPVK/M2CJvOhlsNzi2vusVLoSOJuh3P+JcrQ/f/4eJRng8/asD+S7GW7qabcdatq3UC3GhfzZug6j5U8XEukPEuKUePWXdFZcEQEUHK0WM4BjbO9+cLIZ48eU5umjdvzoEDB/jyyy9ZuXIl9vb21K5dmx9++IFKlSoVRIxCPNYURWHSoUmsPL8SFSomtphIxwodrc4H773FvlUXyEgzorVR06RzBWq3LZXl9gmbzobT99fjlinV1VVXGHL7C4LWRgPg1OVDKl2x5a8/j7H7fAxJ6f90I9vZqGlR0ZOgaubp2t4uBdsVZExMJHFr7v7RY4iOLtBYhBBPjryv3AXUrVuXJUuW5HcsQhQ7iqIw8dBEVpxbYW6xaTHBKrFJjE1jx6+hXA+OBcCvgitPda+Gm0/Wm1YaTQrj1gdbEpv6qvMssp2Gi38aPZp4skXbhm+v+aK6ccpyjZezjqBq3rSr6kPzip7Y2xZs14/+yhUSd+4kacdOUo4dgyzG6GVF6+VVoHEJIZ4cD5Xc3JOWloZer7c6JjOQhDC712JzL7EZ33w8L1Z40XIuZH84+34LQ59mRGOjpkmn8tR+KgB1VptdApiMhB7YRKPE/USqXLkZfIx51bbhosrgsKkKx1p/ii3mpCjA3Z7O9UrSrpoPtUu6Zn/P/HjPjAxSTpwgacdOknbuRH/5stV5m/LlMUZGYkpOzvoGKhVaHx8cGsqYPSFE/shzcpOSksJnn33GypUrs5z2ndddwYUojhRFYcrhKSw/txwVKsY1G0enip0ASLpjbq259re5tcannAvtelTD3dcx+xsGr4NNQ6mRcIs5tvDpljRWHtAz+LKW914MpI9hMGnoLMU/6VCFTnVLFtj7GePiSNqzl6QdO0jauxdTQsI/J21scGzUEKc2bXBq0wbb0qX/mS0F1gOL747x8RkxXAYTCyHyTZ6Tm08//ZQdO3Ywb948unXrxty5c7l58yYLFixgypQpBRGjEI8VRVGYemQqS0OXAjCu2TheqvSSebbSgXD2/nYBfaoBjVZN4IvlqROUQ2sNmBObld3598YFnatq+fqwnppeGn4ztrZKbMC8CWV+v5P+8mVzMrNjJyknTsC//iGjcXfHqXVrnNq0wbFFczRO1mvhuDz9NMyelWmdG62PDz4jhpvPCyFEPsnzIn6lS5fm559/pk2bNri4uHD8+HEqVqzIL7/8wrJlyx75FYplET9RkBRFYdqRafwa8itgTmxervQySXfS2bkklKtnza2d3mXNrTUefjm01oB508tZNVHib3IrUaGkyz/Ts8MTTfg4qYmgBC3SZ2NCbdmEcu/Qp9D8x64oRa8n5dgxEnfsIGnnLjKuXbM6r6tc2dw607YN9rVr56rlRTEazbOnoqPRennh0LCBtNgIIXKlQBfxi42NpXz58oB5fE1srLlpvUWLFvTt2/chwhWieLg/sRnbdCwvVXyJ0IPh7F0ZRnqKAbVWRWDH8tQNCkCdm20Lru4nMfomvdensuuKkZN9HPF1Ml/n52z+vz+3aawO5ZCpOgBjOlZ/6MTGcOcOSbt2kbRzF8l792JKSrKcU9nY4BAYiFPbNji1boNtqbx3e6k0GpnuLYQocHlObsqXL8/ly5cpXbo0VatWZeXKlTRu3Jj169fj5uZWACEK8ehTFIXpR6dbEpvRTUfzjM8LbJx3hiunYwDwLuNMux7V8fB/QGvNv13dh0YNwdEmbqcq7L1m5NXqmZMib+LwdbVjTMfqPFPTL09xp4eFWQYDp548aTUmRuPpiVPrVuYWmmbNUDvmIXYhhCgiee6W+uqrr9BoNAwcOJC//vqLjh07oigKGRkZzJw5k0GDBhVUrPlCuqVEflMUhRlHZ7A4eDEAnweOom58K/asOG9urdGoaNyxHPXal85daw2g3DqJcdt4tBf/AiAk2kh8ukKTUln/e+Tv9kup2vS5XLXYmPR6Ug4dJmmnOaHJuHnT6ryuWjWc25oHA9vVrIlKnT8rFQshxH+Rl+/vPCc397t69SrHjh2jYsWK1K5d+7/cqlBIciPyk6IofHXsK376+ycARtYajcuhKlw+ZW6t8SrtTLse1ShRMpebTUaFEvfHGHpNX81r1W14tYYtemyxV6VzL21RTJASbYshTYPWzoRDhRKoBp8FdfZjVwwxMXe7m3aStG8/SkqK5ZxKp8OxSRNzd1ObNtj4+j5MVQghRIEqsDE3GRkZPPPMM8yfP9+yGnGZMmUoU6bMw0crxGNKURS+On43sVHgE7cvSFpSgtvJMag1Kho9X5Z6HcqgyU1rTewl2DkF5fRKFu5LY3WIga1XVPxW62umtrGn4o5+ACRc1xF53BVD6j+JjPa0Kz61t1nNOFIUhfTQUJJ27iRxx07STp+2epzWy+vuYOC2ODZtgtreHiGEKC7ylNzY2Nhw+r4fkkI8iRRFYdbxWfx09ifsMpx4L24USQftAAOeAU6061Edz1K5aK2JvwG7v4QTv4LJgAqo1rgZfhGptH59AAuGdMHFzga8nEiYN5Sb+zI3tBpiE7g56COU6V+icXKyzG66fz8nu5o1LWvP2NWoXiD7SAkhxKMgz91SH3/8MTqd7rFd00a6pcR/pSgKc07M4fsz31Mhpi5P3+iBkqpGrVbR8Pmy1H8mF601SVGwZyYc/YHYpHS+O6anWbNApmW8ylXbyozvXJPO9f6ZjaQYjVx4qh2GyMhcx6mys8OxWTNzd1Or1tj4eD/kGwshRNEr0KngBoOBH3/8kb/++osGDRrgeN/siZkzZ+b1lkI8NhRF4esTX/Pr8WW0v9STCrH1UIASJZ1o17MaXgHOOd8gJRb2z4FDCyAjBb1RocGPBq7EpOOuVKHtqw3Y2LUuAR7We0ulHD2Wq8RG7e6OyzMdcG7TBofAQNR2BbsxphBCPIrynNycPXuW+vXrA3D+/Hmrc9LMLYozRVH45uQ3bNt5mK6XhmNvcEalVtHg2TI0fLYsGm0OrTVpCXBwHhz4BtLNWxUkedZhZPxLJNQ7j83RdfR67Xkmv9cEbRatPrndMdt35AhcX3jhod5PCCGKi1wnN5cuXaJcuXLs2LGjIOMR4pGkKApzD8zn0oZ0nr79LgAlSjrSrkd1vErn0FqjT4EjC2HvLEg1L3gZ7VCZdc5dmHitGiZFRbV2tZk653OaV81+Ubzc7pit9ZKuJyGEyHVyU6lSJcLDw/H2Nv/w7Nq1K3PmzMHHx6fAghPiUfHt2l9I3e5PxQxnUCk0eKYsjZ4rh8Ymm9YaQzocWwx7pkPS3e6kEpXY6vgSLw6eheL4Iz5vTuGVhmUY16kGznY22T7bGBfHnRUrcg5QdtYWQgiLXCc394873rhxI5MnT873gIR4lKQlZfDD/A1woRQOgNojg1c+aIp3mWwGsxkNcGoZ7JoK8dfNx9xKo7Qexlpjc4b+vIv0tFRsbZIZ1c6f3s/UzfH5SXv2ED7ycwxRUaBWg8lk3klbdtYWQohs5XnMjRDFicmkEB4WR3JCOo4uOvwquVl26L50Mpo/F5+AVBdMmNA1SKRXz05Zt9aYTPD3atgxCWIvmo85+0GrT7hd6TXG/nmJ9af+BgdP2gz6im/7d6Jq6ey7kEzJyURO+5K4uy02tuXK4T91ChkREbKzthBCPECukxuVSpVpwLAMIBaPs4snotizIozkuHTLMUc3HU06led6aCznD0UCWmLtI/B7QaF3u7cy30RRIHQD7JgIUcHmYw4loMVgaNSLb1duZnCHanh0HoG9b3kGtatEvzbPZjlo+J6U48e5NXQYGdfNLT/u3bvh/fHHqO3tsa9dG+d27WRnbSGEyEGu17lRq9U8++yz6HQ6ANavX89TTz2VaSr46tWr8z/KfCTr3AgwJzabFpzNsYwJE6f8t9OkY0V613vX+qSiwMVtsH0C3DphPqZzheYfQmAfMrSOzNkWxqh+3Ui9eIQSNVuyZcP/qF/aPfvn6fXEzJnD7R9+BEVB6+eH/+RJODZp8l9fVwghHnsFss5Njx49rD6//fbbDxedEEXMZFLYsyIs5zIYWVtjDm+06UTvWvclNlf2mZOaa/vNn20coUlfaDYA7N25ejuZQcsPcPJ6HCWe+wifK5vZsGgOPh6u2T4vLSSEW0OHkX53eQXXzp3xGTkCjfMD1s0RQgiRSa6Tm59++qkg4xCi0ISHxVl1RWVFjYYuFbvQu1a3fw7ePGZOai5uN3/W6KDxe9D8I3DyQlEURs9dwsJ1u7Gr9yLOdlomvdGGjnXezPY5isHA7e9/IHruXMjIQOPhgd8X43AOCsqHNxVCiCeTDCgWT5zkhJwTm3vaerY3/yLyb9g+Ec5tMH9Wa6F+d2j1Kbj4AxCfksEHs1exYsTboFLTtnYDfvmoGyXdst+QUn/lCreGDiP11CkAnNsH4Tt2LNoSJR7+5YQQQkhyI548ds7ZryljVU6Jgt9HwNnVgAIqNdR+HdoMBfeylnKHLt3m4xUnuRXvjFPt9tQt48n6cd1wdMg6sVFMJu4sW0bUl9NR0tJQOznh8/lIXDt1kkH6QgiRDyS5EU+cCJeLJNnG4ah3RUXmZEJBIck2jojtb1E6LcV8sMZL0GYEeFW2lMswmhgw/Wc2xbii0thSpoQDq/63jAZls295yQgPJ3zkSJL3HwDAoWkT/CdOxMbfP39fUgghnmCS3IgnTvSFzcTrvHHSu6GgWCU4CubJg/vLrqa2Eaj8LDw1EnxrWd3jSkwyz77zMaF/LMSp3nN8MHwSY1+sgZMu679SiqKQsG4dERMmYkpMRGVnh/eQIbi/9SYq9QN2EBdCCJEnktyIJ4vJSMbWO5RMbI4JI+naVOwNTpbTSbZx7C+7msslTuNVbxrU7mZ1uaIo/HbsBmPX/U2sfSlARZuqvkx7pRbqbJIUQ2wsEWPGkrh1KwB2dWrjP2UKunLlCuw1hRDiSSbJjXiinNu4l5vR5l2z95T/jVDvg/glVMAhw4UUmwTCXS4CJnx1HtSvaT3LKT4lg0+WHGDrxUQAWrdrT78hHWkTWC/b5yVu3074qNEYb98GrRavAf0p0bs3Kq381RNCiIIiP2HFE+Pm+Tts32gAVJzw/4sQb/M6NbdcL1jKqO6uaTnUvz0a9T+r/u4OCadr7wFEn91LwLtz+OTFBvRpXQGNOusBwMakJCInTSb+7qKWukoV8Z86Fbvq1Qvo7YQQQtwjnf3iiXAnIpmN809jMqm4WOIEJ0ut4724BHyMRqtyPkYjM6NiCApoA4DeYGLqplC6fbeHmOADGBOi6FX6Dv3bVsw2sUk+eIjLL3YyJzYqFR693qXs779LYiOEEIVEWm5EsZeaqGfd1yfQpxiJcLrMofK/Mj8qioZp6fSPi+e4nY5ojQYvo5F6aXqMDr4YA5pyNTqJj1ac5PSNeFQ6R94YNoOOlex57eXOWT7HlJZG9FdfEbv4ZwBsSpXCf8pkHBo2LMS3FUIIIcmNKNYMeiNrvzlG0m098boYDtRYyneR4dRIMy/kpwEa3f216e4ua/3jurJ/7Caub/oO27L18KvVnMkv1+K5Wn7ZPif1zBluDR2G/tIlANy6dMH7s8/QODlme40QQoiCIcmNKLYUk8L6748RezWVNE0yh2ovZ0HMBSqkpYJ/fUiMgMRblvIRlGBcRjc2mxoTf+B34o+txyZ0N3u/6k/lAO+sn5GRQcz8BcTMnw9GI1ovL/wmjMepdevCek0hhBD3keRGFFt/rjjKrdNJGFUGTtRZw7dxZymZHAelm0G31RhVNnw4dS7a5CiicOOwqSqmu8PQXBp0Iu3qGcq0eoUKJb2yvH/6hQvcGjqMtL//Nl/z3LP4jBqF1j37nb+FEEIUPEluRLG05c/DXN6VBEBIzb/4Ku0oXomR4FMT3lgGNvYcvnibjYkVgYqYMtJJCdmGY60gVCoVKq0NPl3GkQYcvhxL0wr/rDqsmEzELv6Z6K++QtHrUbu64jt6FK7PP180LyuEEMKKJDei2Nm6dz/n/peMGg3XKx1linovrtFXzftBvb0K7N0AiEpMA0AxGohc8hn6yIuAglPtp63ud68cgP7GTcKHDyflyBEAHFu1xG/8BGx8su62EkIIUfgkuRHFyp9HtxO6LBVb7IkNuMQo9+04XvsbHL2h2xpw9gXM+0LtPh8NgEqjxaFKcwyJt9G4ZE5SvJ3tUBSF+FWriJw0GVNKCioHB3yGDsWty2uy2aUQQjxiJLkRxcbaU39w7ud0nIzupHhFM6TMdhwuHACdC7z9O3iUByAsMpGBvxzk7PUYNPYuALg0eRWnOh3QOLha7qcCfF3tqO9k5EbffiTt3AmAfYMG+E+ehG3p0oX9ikIIIXJBkhtRLCw7s5zQn9Pw1pcmwyWZvrX24xC8ATQ68xgbvzoYTQo/7L3EpKV/cev3SehcPRkycxGLD1xDpVKjui+xAZjqGc3VTp0wxsWhsrHB66OP8OjZA5VGk3UgQgghipwkN+KxpigK35/+gXPLkymXXAuTLoMeTc7gdHoRqNTw6o9QtgXXbqfwyW+nOHwlFn2GEVNiJI42enrUdqJJhfqMWx9MePw/Y2vK25mYemMzjmu3YQR01arhP3UKdpUrF9m7CiGEyB1JbsRjS1EUvjr+FefWx1H7ThsUjYlX2lzD/dRUc4GOs1GqPs/SQ1eZ8EcwqRkmHGw1THr/BTxeK0/9+vXx9vamHNC+ui+HL8cSlZiGf9hp3L75EkNkJKjVlPjgfbz69kVla1uk7yuEECJ3JLkRjyWjyciEQxMI3RVJi4hXAXjmqQT8T31iLtBuDBEVujD0pyNs3XuI25u+pl3ficzv9zwBbjpSjkZgOHyEZC8vHBo2QKPREOhnT+Sv3xC3bDkGwLZsWfynTsG+Tp2ie1EhhBB5JsmNeOxkGDMYsXcEwSeu88yV3gA0bWGg4lnzr5Um/Vnn1IXRs3YTn5pB/K5F6MPD0Bz+BbdAVy5MmowhIsJyP62vL+6vv07cmtVkXL0GgPvbb+M9ZDBqe/vCf0EhhBD/iUpRFKWogyhMCQkJuLq6Eh8fj4uLS1GHI/Io1ZDKkJ1DCDl3mU5/D8TGpKN6HQ1tYrqgMqaRXr0Lg/Xvs+FsFAC1SrryWQtPFsyYwPhOnUgbPQZy+COv9fXFf/IkHJs2LaxXEkIIkQt5+f6Wlhvx2EjSJzFg+wBCr13klXNDsDHpCKhgS6u4bqiMaUT7teGFc69w/dIBDDFXGTHoA/q3rYiNRk2LxYu50C4ox8RGZW9PuTWrZfsEIYR4zKmLOgAhciM2LZZ3N7/L6ZtneeFcXxz0Lnj42NDB1B9NeixX7GvS8nIPrl+5SMQvn3Bn89e0cEvARmP+I55y9JhVV1RWlNRU0s+HFcbrCCGEKEDSciMeeRHJEby/9X2uxF3lxQv9cU/2xcFZywsuo9ElXuGiqjQv3RlIukpHv5facDT2BRSTkfLly1vuYYiOztWzcltOCCHEo+uRaLmZO3cuZcuWxc7OjsDAQA4fPpxt2YULF9KyZUvc3d1xd3cnKCgox/Li8XYt4Ro9/uzB5bjLPH29G353KqK1VfOc7zc4Jx7nhuJJp6tv4OzqzvL3mvD5CzVYtnQJa9euxcPDw3IfY+ztXD1P65X1DuBCCCEeH0We3KxYsYLBgwczZswYjh8/Tp06dejQoQNRUVFZlt+5cydvvPEGO3bs4MCBAwQEBPD0009z8+bNQo5cFLTzd87T/c/u3Eq+RevbL1PuZn1QQYuSv+OTuJkYxYWnDjUl5JcxBMb+RWB5887d9vb2lv2e0i9f5vqAAUROmpzzw1QqtL6+ODRsUNCvJYQQooAVeXIzc+ZM3nvvPd555x2qV6/O/PnzcXBw4Mcff8yy/JIlS+jXrx9169alatWqfP/995hMJrZt21bIkYuCdCr6FO9seofbabdpnvYs1cJaA1DFexc1Un8hUbFnsM3nvPlsW0wGAzeuXsFkMlmuN9y5Q8SEiVzq+CJJf20DjQaHFs3NJ+/f6PLuZ58Rw2VbBSGEKAaKdMyNXq/n2LFjDB8+3HJMrVYTFBTEgQMHcnWPlJQUMjIyrLogxOPtwK0DDNoxiFRDKs01QdQ9+yxGFFyczhCkmkWSQcOishOZ82Y33BxsaVu3Iq1atUKlUmFKT+fOr78SM38BpsREAJzatMH7kyHoKlYkYcsWIu9f58bHB58Rw3F5+umiemUhhBD5qEiTm5iYGIxGIz4+PlbHfXx8CA0NzdU9hg4dir+/P0FBQVmeT09PJz093fI5ISHh4QMWBW7btW18uutTMkwZtHJpR/19L5GWYQDb67zhMIap+/R8f8GTE2e64uRg3g6hdevWKIpCwsaNRM2YScbdLkpdtWr4fPap1Zo1Lk8/jXO7dubZU9HRaO+uUCwtNkIIUXw81rOlpkyZwvLly9m5cyd2dnZZlpk8eTLjxo0r5MjEw1h/cT2j9o3CqBh52vdZau3pRHJSOgZNHH3dPiMh3cTsk3aEx9zg119/pU+fPgCkHD9O5NSppJ06DZhbYrw++gjXFztmmbSoNBocAxsX6rsJIYQoPEWa3Hh6eqLRaIiMjLQ6HhkZia+vb47XTp8+nSlTpvDXX39Ru3btbMsNHz6cwYMHWz4nJCQQEBDw3wIX+W5JyBKmHJ4CQKeynSm14xmSY9LJUKfxnsdgbNVpuD83iuXPBxIaGsp7772H/to1oqbPIHHLFgBUDg6U6N2LEu+8I9smCCHEE6xIkxtbW1saNGjAtm3b6Ny5M4BlcPCAAQOyvW7atGlMnDiRzZs307BhwxyfodPp0Ol0+Rm2yEeKovDd6e/45uQ3AHQu1xX15pYYb6eToeiJCu7NkbLxtOk6AFWrT2ilUtG8dm2ipkwldulSyMgAtRq3V17B88MB2Hh7F/EbCSGEKGpF3i01ePBgevToQcOGDWncuDGzZs0iOTmZd955B4Du3btTsmRJJk82T+WdOnUqo0ePZunSpZQtW5aIuwNDnZyccHJyKrL3EHmnKAozjs5gcfBiANp4v8W1dfVpnGREQSH6wkAm7wpn0WkHQmcNxzkjg9ilS4mZNx9TfDwAji1b4v3JJ9hVqVyUryKEEOIRUuTJTdeuXYmOjmb06NFERERQt25dNm3aZBlkfO3aNdTqf2asz5s3D71ez6uvvmp1nzFjxjB27NjCDF38B0aTkS8OfsHqsNUAlFW9zoWtdXkhxfxHsrXHYsoHRrDlshv9Rk6DQ4e5OGMGGdfMu3brKlfG+7PPcLo3vVsIIYS4S3YFF4Uuw5jBsD3D2HJ1CyrUqG+/htOtBrySoOH89aO8XTeR5rpvoVQjkmuPJ3rWN6QePw6AxssTr4EDcXv5ZZnhJIQQTxDZFVw8slINqXy882P23dyHCg0pN17HOa42nZO0LNg4jHM3j/OUkwP6etWJPlONhOnvAqCys6PEu+9Sote7qB0di/gthBBCPMokuRGFJlGfyIBtAzgedRxMNiTf6IZdUmXeNTmixkg1fz9uREHsDTcuhaajZPwFKhWuL72E16CB2Ny3HpIQQgiRFUluRKG4nXqbD7b24dydUBSjHanXe1LKpjJv2mgxxBtx0cUzq0IYYQnlKHlHh0IGDk2b4PPZZ9hVq1bU4QshhHiMSHIjClxEcgTdNvQiIvUaJoMjqdd60bFUWbZNG8Y8gwP9Ogyj9vGZpESrKYkO2woVzCsL391SQQghhMgLSW5EgTofe4luG3qRYorBlOGKU2x/5r8dRNj/9jH30mlsNLa4HpyBbVoMGlcnvD4egturr6LSyh9NIYQQD0e+QUSB2XjuKMP3D8SkTsSU7kkrl5FMeas117ZfIPWCC93aDuWphGDqJAfj8XIbSoyYjkbWKhJCCPEfSXIj8p3BaGLslg2sDR+PSpOKSu9P/5KfsvGbGRw+epywhHqg1vCqNo16HsfwGjcIm2cHP/jGQgghRC5IciMeitGkcPhyLFGJaXg729G4nAcatYqL0Un0XbWMW7p5qDQZOFOJJZ2+5Z127dl19izX/r7NB881pGTcQdpWWoTDK59AG0lshBBC5B9JbkSebTobzrj1wYTHp1mO+brY0aqyJ+svbEbjsxSV2kgFx3p87/YW8e/0ZrBeTZRfLV5pMQgfJYQXKs9AG/gOtB5ahG8ihBCiOJLkRuTJprPh9P31OOZlrU1oHC6j0iYSbXBmdVgsdn6rUakUWkdUoe6Wm0Tf/Aij2pb4wCH0dyiFmzacFzwmoa31Ijw7DWQ2lBBCiHwmyY3INaNJYdz6YFSKiTop2/C23Uc8KYT4qVDU5iTFPVHhje1uDP3fGn5QFFZUqITx+TEkJLthp07kebcvsKvYCF5aAP/aM0wIIYTIL5LciFw7fDmWciFHmHr2N7xSUizHY5xhSVs1JW8rdDykYJMRQy07O9JdXUn74CuiL9ihUWXwnNsk3MqUhK6/gta2CN9ECCFEcSbJjci1lG1b+fzw4kzHSyTCq6vS8dNqUatUhJW2ZcXCP4nSB3Dg98sAtHOZjZ+fEd78DXQy3VsIIUTBkeRG5IpiNOL787cA3D9KZnNiAqMiInjLvzIuXRpyunQig2/f4OIWAwBNnH6hks8V6LYZHEsUbuBCCCGeOJLciFxJOXoMdUx0luciHUqSbLrFTpMdAxK7UzZEw4UQ85DjavZbqe+5A97eBG4BhRmyEEKIJ5QkNyJX9h0K5d+piaIoqFQqojzrUKp6b94tu5s65VqiVmvullABCgG2J1E1HwjeVYsgaiGEEE8ima4icqQoCnO2hfH1qTuWY+vi4+lx/RrpJoWwiq+hUqmoX6ENGktiY7ma/Yk9MR3+AUzGwg1cCCHEE0uSG5Etg9HEiDVnmLn1HOVNu1GAeKORKdFRHE1NZbFeS7qdew5r1ahJMnkRHuMKV/cXZuhCCCGeYNItJbKUojcwYOkJdodc4+Ow2TwdbB5v46LRMNnXjzNpqTwfUJtzubhXsskdkiILNmAhhBDiLmm5EZnEJKXz+ncHOXHmNFMPjyf5QBin01IJ7x5EqdmzaVexIgM8vdDbe+fqfo7qO+DkU8BRCyGEEGbSciOsXI5JpsePh7G7vp85B5ey5dptJkRFUtLHk+ABE3FxcUHdqAU7vzvC1Sv3xtEoZJ4gDmDCSX0bP894KNOsEN9CCCHEk0ySG2Fx/Nodei8+Sr3Lqxl4eCe2BggsX4Iydlre/6Afjo6OBO+7xf5VF0hPMaLCSFndIS6nNwVMWDcEmgAVLVx+RP3sZMg02FgIIYQoGJLcCAC2BkcyaMlh3gpZQKVjZ7G1t+dCDTeaz/+NUGdP0uJNrJ9zipvn4gDwtrlAW5e5eHopXKzehj1b0khO+ud+TurbtPBZS4XX+kP1F4vmpYQQQjyRJLkR/HLwKl/+doARJ2ex7EgwnycmMuLlZoxZvh2N2oaTW69xZMMVjBkmtKoMAp1+pbbDH6jrvQHPTKGCnQvlghTCz8eSfPEsjpo7+FXyQ13uV2mxEUIIUegkuXmCKYrCl5vP8ccf25h1ZAF+8Rmss9GgVqso2e4t4m6ls/2XU9y+YW6SCdCdprXzt7g66+GFn6BGZ8u91GoVJauWgKqti+hthBBCCDNJbp5QeoOJYatOc2vTSr48vAYXI0S7qXh/9nQGO9fEeMuD36ccRVFAp02jheMCqtjtRFWhLXT+Flz8i/oVhBBCiCxJcvMESkzLoO8vR/H5YzZ3tm9kilpNz2ZlqTJ3IY5pFdi5NJSEmOsAVHI6SAv7+TjYpkH7KdD4A1DLCgJCCCEeXZLcPGEiE9J4f/5uOv01DZvQMGYlJaJSqxg0fBY3dzsSevAkAE52KbS2n0FZ3XHwqQWvLATvakUbvBBCCJELktw8QcIiExkyaz39ds6kzJ1kMpwd6NqxAS92Hsul9RpSEyMAqOWxhybab7FVp0OzgfDU56DVFXH0QgghRO5IcvOEOHTpNtPGf4t63Uyc3T2JddNycdDbvBn/AlcP3wYy8HBNoa12PL42oeBSCl6aB+VaFXXoQgghRJ5IcvME+OPUTXZOnkrkHz+yLzmZCLWRwaN+Ie2oG1fTb6PWQEOfXdQ3foNGZYCar8LzM8DerahDF0IIIfJMkpti7sdtIaR8OYJeV0Jp6eXFFa2K516bTvQBZ8CIn086bVSj8DCFgZ2rOamp/VpRhy2EEEI8NEluiimTSeGL+X+gmzeCzhkGMtRaLnfszjBDS1QmNTY6Nc1K76FGwgxUKFCmBbw0H9wCijp0IYQQ4j+R5KYYSssw8tnw2SyfM4w0o5FS9Vpyp/mH2KS7A1C2vEJr46c4JZ4HjQ20GwVNB8hqwkIIIYoFSW6KmbgUPfM+nUaP7UvYo7Ul0s6OcxW645fujr2zDa0qH6LCjfGoVIBXVXh5IfjVLuqwhRBCiHwjyU0xcibsKnuGjqNz8AFiStSkS/0h6Oz80NnYU62uLc3SRmB38xSogMC+EDQGbOyLOmwhhBAiX0ly8xhSjEZSjh7DEB2N1ssLh4YNWPTzSgb36UmfEr6cbTKAKO8GuAAuXna0qX2WgJDhYDKAk695+4SK7Yr6NYQQQogCIcnNYyZhyxYiJ03GEBFhOWZyc+fktavE6fUs0Wvx96yLRg11W7jRKHkMNn/vNhes1hE6zgEHjyKKXgghhCh4ktw8RhK2bOHmoI9AUayOp6VpaNxiJK/dCqVJlQ6UKO1E+yY38TrSA9ITwNYJnp0Gdd/EPNhGCCGEKL4kuXlMKEYjkZMmWxKb/cnJrIyPY2Cjblwr3xGTxpY2bhVo9EJJGqTOQL13tfnCgEB4aQF4lCvC6IUQQojCI8nNYyLl6DFLV1SC0chH4eEkGQ04p0FbjS3ud0Kpcm4ZVZ1jUDvfArUWWg+DFh+DRn6bhRBCPDnkW+8xYYiOBsCotiGqfGdeK63ifPhpWldsS7XQX/CNOIgKMMTegbIV4eXvoGSDog1aCCGEKAKS3DyCTCaF8LA4khPScXTR4VfJje1hYSTb+qGv9wGp9l40AJ51daPy8WnYZiRartXWeRo+mAe2jkX3AkIIIUQRkuTmEXPxRBR7VoSRHJduOXbk6mYWb5qGn3tZPm3kjHPaHaqELcfz9tl/Xamg9XTH4cOfQCMrDQshhHhySXLzCLl4IopNC84CCuaV9swqezXCxcGDSv51KBm+j6pXN2JjTLvvahU+o8ehksRGCCHEE06Sm0eEyaSwZ0UY9xKbmIRbeLr4A+Dq4MGI1xaidtIQ7j2fehEpGFLUlmu1vr74jBiOy9NPF03wQgghxCNEkptHRHhYHMlx6ZhMJtYe+o6dZ9cw6IUZVPCrBYCTnRsYYK+zDf1fiCBdqYGhei+05Wvh0LCBtNgIIYQQd0ly84hITjCPsVGp1CSm3MFkMnIh/LQlubknzeTOiSY9aNRhhnlHbyGEEEJYkeTmEeHgbAuASqWia8uPaFipHTVKB2Yql2KTQHTVrpLYCCGEENmQ5KYIGU0Ke0PDmfbFSBztbGnn1ZWMRAU7W4dMiY2CQpJtHOEuF/Fy8CqiiIUQQohHnyQ3RWTT2XDGrQ/m0ulDRK1YBKi41aMer9tVQkFB9a/ZUgrmLRcOlF2Dj6MP9b3rF03QQgghxGNAkpsisOlsOP1/PUojdSiNyiWzo1UHor0DSfd1xmizlLT453DSu1vKJ9nGcaDsGi6XOM3MxjPRqGXwsBBCCJEdSW4KWXJKKl8MeJMNgZeoahtnPtgWYpUz2GDAWZXGxpJb+NmpBelptqTYJBDuchEfRx9mNp5JUJmgIo1fCCGEeNRJclPIXmzfggP7jzM2RsvyVx0sxz1USQBcM3nxVdynjH3lJWydrhKdEo2Xgxf1vetLi40QQgiRC5LcFCaTkdF1ojl3RkWveraZTisKaFVGLir+xCRl0KlioyIIUgghhHi8qR9cRPwXKSkpnDp1yvzh6n5ae8dzcaAT7StkzitVKvBXxdJYHYq3s10hRyqEEEIUD5LcFKBr167RuHFj2rdvz61btyApEgCdVpXjdZUdkmlczqMwQhRCCCGKnUciuZk7dy5ly5bFzs6OwMBADh8+nGP53377japVq2JnZ0etWrXYuHFjIUWaN97e3mg0GjQaDTdu3AAnn1xd90KzumjUOSdAQgghhMhakSc3K1asYPDgwYwZM4bjx49Tp04dOnToQFRUVJbl9+/fzxtvvEGvXr04ceIEnTt3pnPnzpw9e7aQI89aenq65dd2dnasWbOGkydP0rhxYyjTDFz8+feO3/9mAlLtfWncpmPhBCuEEEIUQypFUZSiDCAwMJBGjRrxzTffAGAymQgICODDDz9k2LBhmcp37dqV5ORk/vjjD8uxJk2aULduXebPn//A5yUkJODq6kp8fDwuLi759yLA6dOn6dq1KyNGjKBbt25ZFwpeByu73/3wT9UrdxMeVZefofqL+RqXEEII8bjLy/d3kbbc6PV6jh07RlDQP2u3qNVqgoKCOHDgQJbXHDhwwKo8QIcOHbItn56eTkJCgtV/BeV///sfoaGhTJgwAYPBkHWh6i9Cl5/Bxc/qsMrFXxIbIYQQIh8U6VTwmJgYjEYjPj7WY1F8fHwIDQ3N8pqIiIgsy0dERGRZfvLkyYwbNy5/An6AESNGoNfrGThwIFptDlVb/UWo+jxc3W8eZOzkY+6yknVshBBCiP+s2K9zM3z4cAYPHmz5nJCQQEBAQIE8S6PRMH78+NwVVmugXMsCiUMIIYR4khVpcuPp6YlGoyEyMtLqeGRkJL6+vlle4+vrm6fyOp0OnU6XPwELIYQQ4pFXpGNubG1tadCgAdu2bbMcM5lMbNu2jaZNm2Z5TdOmTa3KA2zdujXb8kIIIYR4shR5t9TgwYPp0aMHDRs2pHHjxsyaNYvk5GTeeecdALp3707JkiWZPHkyAIMGDaJ169bMmDGD559/nuXLl3P06FG+++67onwNIYQQQjwiijy56dq1K9HR0YwePZqIiAjq1q3Lpk2bLIOGr127hlr9TwNTs2bNWLp0KZ9//jkjRoygUqVKrF27lpo1axbVKwghhBDiEVLk69wUtoJc50YIIYQQBeOxWedGCCGEECK/SXIjhBBCiGJFkhshhBBCFCuS3AghhBCiWJHkRgghhBDFiiQ3QgghhChWJLkRQgghRLEiyY0QQgghihVJboQQQghRrBT59guF7d6CzAkJCUUciRBCCCFy6973dm42VnjikpvExEQAAgICijgSIYQQQuRVYmIirq6uOZZ54vaWMplM3Lp1C2dnZ1QqVb7eOyEhgYCAAK5fvy77VhUgqefCIfVcOKSeC4/UdeEoqHpWFIXExET8/f2tNtTOyhPXcqNWqylVqlSBPsPFxUX+4hQCqefCIfVcOKSeC4/UdeEoiHp+UIvNPTKgWAghhBDFiiQ3QgghhChWJLnJRzqdjjFjxqDT6Yo6lGJN6rlwSD0XDqnnwiN1XTgehXp+4gYUCyGEEKJ4k5YbIYQQQhQrktwIIYQQoliR5EYIIYQQxYokN0IIIYQoViS5yaO5c+dStmxZ7OzsCAwM5PDhwzmW/+2336hatSp2dnbUqlWLjRs3FlKkj7e81PPChQtp2bIl7u7uuLu7ExQU9MDfF2GW1z/P9yxfvhyVSkXnzp0LNsBiIq/1HBcXR//+/fHz80On01G5cmX52ZELea3nWbNmUaVKFezt7QkICODjjz8mLS2tkKJ9PO3evZuOHTvi7++PSqVi7dq1D7xm586d1K9fH51OR8WKFVm0aFGBx4kicm358uWKra2t8uOPPyp///238t577ylubm5KZGRkluX37dunaDQaZdq0aUpwcLDy+eefKzY2NsqZM2cKOfLHS17r+c0331Tmzp2rnDhxQgkJCVF69uypuLq6Kjdu3CjkyB8vea3ney5fvqyULFlSadmypdKpU6fCCfYxltd6Tk9PVxo2bKg899xzyt69e5XLly8rO3fuVE6ePFnIkT9e8lrPS5YsUXQ6nbJkyRLl8uXLyubNmxU/Pz/l448/LuTIHy8bN25URo4cqaxevVoBlDVr1uRY/tKlS4qDg4MyePBgJTg4WPn6668VjUajbNq0qUDjlOQmDxo3bqz079/f8tloNCr+/v7K5MmTsyzfpUsX5fnnn7c6FhgYqHzwwQcFGufjLq/1fD+DwaA4OzsrixcvLqgQi4WHqWeDwaA0a9ZM+f7775UePXpIcpMLea3nefPmKeXLl1f0en1hhVgs5LWe+/fvrzz11FNWxwYPHqw0b968QOMsTnKT3Hz22WdKjRo1rI517dpV6dChQwFGpijSLZVLer2eY8eOERQUZDmmVqsJCgriwIEDWV5z4MABq/IAHTp0yLa8eLh6vl9KSgoZGRl4eHgUVJiPvYet5y+++AJvb2969epVGGE+9h6mntetW0fTpk3p378/Pj4+1KxZk0mTJmE0Ggsr7MfOw9Rzs2bNOHbsmKXr6tKlS2zcuJHnnnuuUGJ+UhTV9+ATt3Hmw4qJicFoNOLj42N13MfHh9DQ0CyviYiIyLJ8REREgcX5uHuYer7f0KFD8ff3z/QXSvzjYep57969/PDDD5w8ebIQIiweHqaeL126xPbt23nrrbfYuHEjFy5coF+/fmRkZDBmzJjCCPux8zD1/OabbxITE0OLFi1QFAWDwUCfPn0YMWJEYYT8xMjuezAhIYHU1FTs7e0L5LnSciOKlSlTprB8+XLWrFmDnZ1dUYdTbCQmJtKtWzcWLlyIp6dnUYdTrJlMJry9vfnuu+9o0KABXbt2ZeTIkcyfP7+oQytWdu7cyaRJk/j22285fvw4q1evZsOGDYwfP76oQxP5QFpucsnT0xONRkNkZKTV8cjISHx9fbO8xtfXN0/lxcPV8z3Tp09nypQp/PXXX9SuXbsgw3zs5bWeL168yJUrV+jYsaPlmMlkAkCr1XLu3DkqVKhQsEE/hh7mz7Ofnx82NjZoNBrLsWrVqhEREYFer8fW1rZAY34cPUw9jxo1im7dutG7d28AatWqRXJyMu+//z4jR45ErZZ/++eH7L4HXVxcCqzVBqTlJtdsbW1p0KAB27ZtsxwzmUxs27aNpk2bZnlN06ZNrcoDbN26Ndvy4uHqGWDatGmMHz+eTZs20bBhw8II9bGW13quWrUqZ86c4eTJk5b/XnzxRdq2bcvJkycJCAgozPAfGw/z57l58+ZcuHDBkjwCnD9/Hj8/P0lssvEw9ZySkpIpgbmXUCqy5WK+KbLvwQIdrlzMLF++XNHpdMqiRYuU4OBg5f3331fc3NyUiIgIRVEUpVu3bsqwYcMs5fft26dotVpl+vTpSkhIiDJmzBiZCp4Lea3nKVOmKLa2tsrvv/+uhIeHW/5LTEwsqld4LOS1nu8ns6VyJ6/1fO3aNcXZ2VkZMGCAcu7cOeWPP/5QvL29lQkTJhTVKzwW8lrPY8aMUZydnZVly5Yply5dUrZs2aJUqFBB6dKlS1G9wmMhMTFROXHihHLixAkFUGbOnKmcOHFCuXr1qqIoijJs2DClW7dulvL3poJ/+umnSkhIiDJ37lyZCv4o+vrrr5XSpUsrtra2SuPGjZWDBw9azrVu3Vrp0aOHVfmVK1cqlStXVmxtbZUaNWooGzZsKOSIH095qecyZcooQKb/xowZU/iBP2by+uf53yS5yb281vP+/fuVwMBARafTKeXLl1cmTpyoGAyGQo768ZOXes7IyFDGjh2rVKhQQbGzs1MCAgKUfv36KXfu3Cn8wB8jO3bsyPLn7b267dGjh9K6detM19StW1extbVVypcvr/z0008FHqdKUaT9TQghhBDFh4y5EUIIIUSxIsmNEEIIIYoVSW6EEEIIUaxIciOEEEKIYkWSGyGEEEIUK5LcCCGEEKJYkeRGCCGEEMWKJDdCiCLRs2dPOnfubPncpk0bPvroo0KPY+fOnahUKuLi4gr92VeuXEGlUv3nndbvr8us3F+/ZcuWZdasWZbPKpWKtWvX/qc4hHhUSHIjRAHr2bMnKpWKPn36ZDrXv39/VCoVPXv2LPzAHjGrV6/O9Y7MRZmQPK4eVL/h4eE8++yzQP4lXUIUFUluhCgEAQEBLF++nNTUVMuxtLQ0li5dSunSpYswsv9Gr9fn2708PDxwdnbOt/sVtYyMjKIOwcqD6tfX1xedTleIEQlRcCS5EaIQ1K9fn4CAAFavXm05tnr1akqXLk29evWsyppMJiZPnky5cuWwt7enTp06/P7775bzRqORXr16Wc5XqVKF2bNnW93jXjfF9OnT8fPzo0SJEvTv3z/HL9yxY8dSt25dFixYQEBAAA4ODnTp0oX4+PhM9504cSL+/v5UqVIFgOvXr9OlSxfc3Nzw8PCgU6dOXLlyxSrmwYMH4+bmRokSJfjss88y7bx8f7dJeno6Q4cOJSAgAJ1OR8WKFfnhhx+4cuUKbdu2BcDd3d2q5etBdQewceNGKleujL29PW3btrWKMzsqlYp58+bx7LPPYm9vT/ny5a3ue6+lY8WKFbRu3Ro7OzuWLFmCyWTiiy++oFSpUuh0OurWrcumTZsy3T80NJRmzZphZ2dHzZo12bVrl1XdPej3+55x48bh5eWFi4sLffr0sUo+H9Tt9+9uqXLlygFQr149VCoVbdq0Yffu3djY2BAREWF13UcffUTLli0fWIdCFCZJboQoJO+++y4//fST5fOPP/7IO++8k6nc5MmT+fnnn5k/fz5///03H3/8MW+//bblC89kMlGqVCl+++03goODGT16NCNGjGDlypVW99mxYwcXL15kx44dLF68mEWLFrFo0aIcY7xw4QIrV65k/fr1bNq0iRMnTtCvXz+rMtu2bePcuXNs3bqVP/74g4yMDDp06ICzszN79uxh3759ODk58cwzz1i+XGfMmMGiRYv48ccf2bt3L7GxsaxZsybHWLp3786yZcuYM2cOISEhLFiwACcnJwICAli1ahUA586dIzw83PJl/6C6u379Oi+//DIdO3bk5MmT9O7dm2HDhuUYxz2jRo3ilVde4dSpU7z11lu8/vrrhISEWJUZNmwYgwYNIiQkhA4dOjB79mxmzJjB9OnTOX36NB06dODFF18kLCzM6rpPP/2UIUOGcOLECZo2bUrHjh25ffs2kPvf723bthESEsLOnTtZtmwZq1evZty4cbl6t/sdPnwYgL/++ovw8HBWr15Nq1atKF++PL/88oulXEZGBkuWLOHdd999qOcIUWAKfGtOIZ5w93bPjoqKUnQ6nXLlyhXlypUrip2dnRIdHa106tTJsqNuWlqa4uDgoOzfv9/qHr169VLeeOONbJ/Rv39/5ZVXXrF6ZpkyZax2kn7ttdeUrl27ZnuPMWPGKBqNRrlx44bl2J9//qmo1WolPDzccl8fHx8lPT3dUuaXX35RqlSpophMJsux9PR0xd7eXtm8ebOiKIri5+enTJs2zXI+IyNDKVWqlNWu4q1bt1YGDRqkKIqinDt3TgGUrVu3ZhnrvZ2J/72Dc27qbvjw4Ur16tWtzg8dOjTTve4HKH369LE6FhgYqPTt21dRFEW5fPmyAiizZs2yKuPv769MnDjR6lijRo2Ufv36WV03ZcoUy/l7dTN16tRs48nq99vDw0NJTk62HJs3b57i5OSkGI1GRVGs61dRFKVMmTLKV199ZfWOa9assYrrxIkTVs+dOnWqUq1aNcvnVatWKU5OTkpSUlK2sQpRFLRFl1YJ8WTx8vLi+eefZ9GiRSiKwvPPP4+np6dVmQsXLpCSkkL79u2tjuv1eqvuq7lz5/Ljjz9y7do1UlNT0ev11K1b1+qaGjVqoNFoLJ/9/Pw4c+ZMjjGWLl2akiVLWj43bdoUk8nEuXPn8PX1BaBWrVrY2tpaypw6dYoLFy5kGs+RlpbGxYsXiY+PJzw8nMDAQMs5rVZLw4YNM3VN3XPy5Ek0Gg2tW7fOMd5/y03dhYSEWMVx7x1z4/5yTZs2zTTgtmHDhpZfJyQkcOvWLZo3b25Vpnnz5pw6dSrbe9+rm3+3CuXm97tOnTo4ODhY3TMpKYnr169TpkyZXL3jg/Ts2ZPPP/+cgwcP0qRJExYtWkSXLl1wdHTMl/sLkV8kuRGiEL377rsMGDAAMH9h3S8pKQmADRs2WCUZgGWw5/Lly/nkk0+YMWMGTZs2xdnZmS+//JJDhw5ZlbexsbH6rFKpMJlM//kd7v8iS0pKokGDBixZsiRTWS8vr4d6hr29fZ6vyU3dFbSC+JLP7e93YfD29qZjx4789NNPlCtXjj///JOdO3cWehxCPIgkN0IUonvjUFQqFR06dMh0vnr16uh0Oq5du5Ztq8W+ffto1qyZ1ViYixcv5kt8165d49atW/j7+wNw8OBB1Gq1ZeBwVurXr8+KFSvw9vbGxcUlyzJ+fn4cOnSIVq1aAWAwGDh27Bj169fPsnytWrUwmUzs2rWLoKCgTOfvtRwZjUbLsdzUXbVq1Vi3bp3VsYMHD2b7bveX6969u9Xn+weD/5uLiwv+/v7s27fPKp59+/bRuHHjTPe+v27uJcG5/f0+deoUqamplsTw4MGDljFKeZVV/d7Tu3dv3njjDUqVKkWFChUytUwJ8SiQAcVCFCKNRkNISAjBwcFWXUb3ODs788knn/Dxxx+zePFiLl68yPHjx/n6669ZvHgxAJUqVeLo0aNs3ryZ8+fPM2rUKI4cOZIv8dnZ2dGjRw9OnTrFnj17GDhwIF26dLF0SWXlrbfewtPTk06dOrFnzx4uX77Mzp07GThwIDdu3ABg0KBBTJkyhbVr1xIaGkq/fv1yXKOmbNmy9OjRg3fffZe1a9da7nlvEG2ZMmVQqVT88ccfREdHk5SUlKu669OnD2FhYXz66aecO3eOpUuXPnCQ9T2//fYbP/74I+fPn2fMmDEcPnzYkoBk59NPP2Xq1KmsWLGCc+fOMWzYME6ePMmgQYOsys2dO5c1a9YQGhpK//79uXPnjmWQbm5/v/V6Pb169SI4OJiNGzcyZswYBgwYgFqd9x/z3t7e2Nvbs2nTJiIjI61mzHXo0AEXFxcmTJiQ5YB4IR4FktwIUchcXFyybeEAGD9+PKNGjWLy5MlUq1aNZ555hg0bNlim537wwQe8/PLLdO3alcDAQG7fvp1pRtPDqlixIi+//DLPPfccTz/9NLVr1+bbb7/N8RoHBwd2795N6dKlefnll6lWrRq9evUiLS3N8p5DhgyhW7du9OjRw9K18tJLL+V433nz5vHqq6/Sr18/qlatynvvvUdycjIAJUuWZNy4cQwbNgwfHx9LkvGguitdujSrVq1i7dq11KlTh/nz5zNp0qRc1c24ceNYvnw5tWvX5ueff2bZsmVUr149x2sGDhzI4MGDGTJkCLVq1WLTpk2sW7eOSpUqWZWbMmUKU6ZMoU6dOuzdu5d169ZZxmPl9ve7Xbt2VKpUiVatWtG1a1defPFFxo4dm6t3u59Wq2XOnDksWLAAf39/OnXqZDmnVqvp2bMnRqPRqiVLiEeJSsluRJ8Q4okyduxY1q5dK6vSZkGlUrFmzZoHbnHwpOjVqxfR0dGZuviEeFTImBshhBC5Eh8fz5kzZ1i6dKkkNuKRJsmNEEKIXOnUqROHDx+mT58+mabcC/EokW4pIYQQQhQrMqBYCCGEEMWKJDdCCCGEKFYkuRFCCCFEsSLJjRBCCCGKFUluhBBCCFGsSHIjhBBCiGJFkhshhBBCFCuS3AghhBCiWJHkRgghhBDFyv8Btc3y8QWWrlQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example dataframe with probabilities for 5 classes and actual labels\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Number of classes\n",
    "n_classes = 5\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Compute and plot calibration curve for each class\n",
    "for i in range(n_classes):\n",
    "    # Extract probabilities for the current class\n",
    "    probs = df[f'class_{i}_probs']\n",
    "    # Create binary labels for the current class\n",
    "    labels = (df['actual_labels'] == i).astype(int)\n",
    "\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=10, normalize=False)\n",
    "\n",
    "    # Plot\n",
    "    ax.plot(prob_pred, prob_true, marker='o', label=f'Class {i}')\n",
    "\n",
    "# Plot perfectly calibrated line\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n",
    "\n",
    "ax.set_xlabel(\"Mean predicted probability\")\n",
    "ax.set_ylabel(\"Fraction of positives\")\n",
    "ax.set_title(\"Babbage Calibration (FT)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('babbage.png', dpi=600)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rEcD8flQBnXR",
   "metadata": {
    "id": "rEcD8flQBnXR"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "j8Ti-eAw8Hp_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j8Ti-eAw8Hp_",
    "outputId": "6c55ac68-b83c-42cc-9740-102f66fc3f88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (generated): 0.5594552121529597\n",
      "Accuracy (top token): 0.7073511437052558\n",
      "Log Loss: 0.8295388079586312\n",
      "ECE per class: [0.011941165449045419, 0.012911575529423786, 0.009528249367511376, 0.009277180271479155, 0.019928434395505897]\n",
      "Average ECE: 0.012717321002593127\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "\n",
    "\n",
    "a = (df['predicted_token'] == df['actual_labels'].astype(str)).mean()\n",
    "print(f'Accuracy (generated): {a}')\n",
    "\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "print(f'Accuracy (top token): {accuracy}')\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aJqNvigXXcCd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aJqNvigXXcCd",
    "outputId": "6efb5744-e2b8-49e9-928a-616eafe78a46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7073511437052558\n",
      "Log Loss: 0.8295387361482707\n",
      "ECE per class: [0.011941004373774244, 0.012911764913034834, 0.009528137342639298, 0.009277618857687605, 0.019893728784770203]\n",
      "Average ECE: 0.012710450854381236\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2p8NlR-U2o2-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "a9b80c429df4415aad8f068808597e9e",
      "1083e900520140d48f2089d48423f008",
      "7a156ca12ec04f64b2ec7b1ca09c2b55",
      "d25d2da4888e45cf97023339c5d8f9b8",
      "d15a2c2e4d7f4027b1ae88d7514459c3",
      "b8e112ff1135425882dac5043b4c8312",
      "548d27611c4642e3b9f42e9a1c1c6f73",
      "e29cafccd7cd46be997d0c4b40d18afe",
      "f35abe28e9254630b7d34ce391facf6a",
      "63b2eb7a6da945d382a9069c3ba3708e",
      "1b8f06268a904169a265c03ff9fb7ba5"
     ]
    },
    "id": "2p8NlR-U2o2-",
    "outputId": "e428dcf7-476c-433a-e4a0-15921326fd38"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9b80c429df4415aad8f068808597e9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5762 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpt_3_5_data = {\n",
    "    'class_0_probs': [],\n",
    "    'class_1_probs': [],\n",
    "    'class_2_probs': [],\n",
    "    'class_3_probs': [],\n",
    "    'class_4_probs': [],\n",
    "    'actual_labels': []\n",
    "}\n",
    "\n",
    "for test in tqdm(test_sample):\n",
    "    _data = {}\n",
    "    try:\n",
    "        predicted_label, probs = run_ft_model(test['review']+'\\n###\\n', 'ft:gpt-3.5-turbo-0125:personal::9PdLZ86e', chat=True, system='') # gpt3. no systme 2 epoch\n",
    "    except:\n",
    "        continue\n",
    "    for key, prob in probs.items():\n",
    "        if str(key.lower().strip()) in ['0', '1', '2', '3', '4']:\n",
    "            _data[f'class_{key.lower().strip()}_probs'] = prob\n",
    "    if len(_data) >= 5:\n",
    "        for key, value in _data.items():\n",
    "            gpt_3_5_data[key].append(value)\n",
    "        gpt_3_5_data['actual_labels'].append(test['star'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0VsY0ejN2ozp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "0VsY0ejN2ozp",
    "outputId": "83cf60c4-0872-4f22-cda3-e74ba2da8fd8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example dataframe with probabilities for 5 classes and actual labels\n",
    "\n",
    "df = pd.DataFrame(gpt_3_5_data)\n",
    "\n",
    "# Number of classes\n",
    "n_classes = 5\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Compute and plot calibration curve for each class\n",
    "for i in range(n_classes):\n",
    "    # Extract probabilities for the current class\n",
    "    probs = df[f'class_{i}_probs']\n",
    "    # Create binary labels for the current class\n",
    "    labels = (df['actual_labels'] == i).astype(int)\n",
    "\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=10, normalize=False)\n",
    "\n",
    "    # Plot\n",
    "    ax.plot(prob_pred, prob_true, marker='o', label=f'Class {i}')\n",
    "\n",
    "# Plot perfectly calibrated line\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n",
    "\n",
    "ax.set_xlabel(\"Mean predicted probability\")\n",
    "ax.set_ylabel(\"Fraction of positives\")\n",
    "ax.set_title(\"GPT 3.5 0125 Calibration (FT)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('gpt_ft.png', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Tocgt_HOXaI2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tocgt_HOXaI2",
    "outputId": "9bacf503-75ec-4b00-e6d6-99b08d880a99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7294342242276988\n",
      "Log Loss: 0.7763683281332503\n",
      "ECE per class: [0.012396305102395009, 0.013063910100659364, 0.00998414175055592, 0.019263380948142183, 0.03495337035751499]\n",
      "Average ECE: 0.017932221651853493\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "azfiFIRo2owX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "a64874fa680e47c8905119c8ea0b9e99",
      "7cbe5975f3a84e7192a10a5d4e01bae9",
      "d2aa2130c4984527843e61ed466e1897",
      "469de7e60a29447a88c77619d13c73c9",
      "980d17cdecb74229916cdb893cb288fb",
      "25816bd536d145b0b46adb188d1bcb8d",
      "b119766fd2ed4c90927619b40ea4920e",
      "f2e2f1171ecf44b9988162f5ef378967",
      "62db7aa582ac4d7eb84c87f7a64f0d64",
      "04d2df415df74fc6ab3fabdd2e36a4f6",
      "e765956918f24dea9f41d7b3449961ee"
     ]
    },
    "id": "azfiFIRo2owX",
    "outputId": "8fc013c6-482f-49d1-9020-c3ba7f8f514c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a64874fa680e47c8905119c8ea0b9e99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5762 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_ft_gpt_3_5_data = {\n",
    "    'class_0_probs': [],\n",
    "    'class_1_probs': [],\n",
    "    'class_2_probs': [],\n",
    "    'class_3_probs': [],\n",
    "    'class_4_probs': [],\n",
    "    'actual_labels': []\n",
    "}\n",
    "\n",
    "for test in tqdm(test_sample):\n",
    "    _data = {}\n",
    "    try:\n",
    "        predicted_label, probs = run_ft_model(\n",
    "            f'Please classify this app review with 0-4 where 0 means they hated it, 2 means they kind of liked it, and 4 means they loved it.\\n\\nReview: {test[\"review\"]}\\nRating (0, 1, 2, 3, or 4):',\n",
    "            'gpt-3.5-turbo-0125', chat=True, system='')\n",
    "    except:\n",
    "        continue\n",
    "    for key, prob in probs.items():\n",
    "        if str(key.lower().strip()) in ['0', '1', '2', '3', '4'] and f'class_{key.lower().strip()}_probs' not in _data:\n",
    "            _data[f'class_{key.lower().strip()}_probs'] = prob\n",
    "    if len(_data) >= 5:\n",
    "        for key, value in _data.items():\n",
    "            non_ft_gpt_3_5_data[key ].append(value)\n",
    "        non_ft_gpt_3_5_data['actual_labels'].append(test['star'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BNO4tGWe2os8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "BNO4tGWe2os8",
    "outputId": "4190f46f-4c48-4f2a-d994-0d9fd59548b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example dataframe with probabilities for 5 classes and actual labels\n",
    "\n",
    "df = pd.DataFrame(non_ft_gpt_3_5_data)\n",
    "\n",
    "# Number of classes\n",
    "n_classes = 5\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Compute and plot calibration curve for each class\n",
    "for i in range(n_classes):\n",
    "    # Extract probabilities for the current class\n",
    "    probs = df[f'class_{i}_probs']\n",
    "    # Create binary labels for the current class\n",
    "    labels = (df['actual_labels'] == i).astype(int)\n",
    "\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=10, normalize=False)\n",
    "\n",
    "    # Plot\n",
    "    ax.plot(prob_pred, prob_true, marker='o', label=f'Class {i}')\n",
    "\n",
    "# Plot perfectly calibrated line\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n",
    "\n",
    "ax.set_xlabel(\"Mean predicted probability\")\n",
    "ax.set_ylabel(\"Fraction of positives\")\n",
    "ax.set_title(\"GPT 3.5 0125 Calibration (non FT)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('gpt_non_ft.png', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UvP9tA6S9Qna",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UvP9tA6S9Qna",
    "outputId": "b3eb26d0-0040-46b0-aa17-4b8d5f0a7a43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3401563081503536"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_label = []\n",
    "# for i, row in df.iterrows():\n",
    "    # print(np.array([row['class_0_probs'], row['class_1_probs'], row['class_2_probs'], row['class_3_probs'], row['class_4_probs']]).argmax())\n",
    "predicted_labels = [np.array([row['class_0_probs'], row['class_1_probs'], row['class_2_probs'], row['class_3_probs'], row['class_4_probs']]).argmax() for i, row in df.iterrows()]\n",
    "\n",
    "(np.array(predicted_labels)==df['actual_labels']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2kzpouO3XXeB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2kzpouO3XXeB",
    "outputId": "033682db-be0d-41fa-b15f-590380ba6f21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3401563081503536\n",
      "Log Loss: 4.3908020170346\n",
      "ECE per class: [0.09067929289170082, 0.14689553033122446, 0.2923829251953852, 0.15829091179754376, 0.3465911164867883]\n",
      "Average ECE: 0.2069679553405285\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
  },
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    },
    "id": "EU4vS1zTUSqt",
    "outputId": "f9e301c3-03bb-4e60-e0a0-1bc4a757239c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2db9dc79a8c1461b8340752b8b90ac9e",
       "version_major": 2,
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/866 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "332d17cc73ed4a9490b94d9f0d022770",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/263M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a42fcb28ac294b06843e633947c4af1a",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e5359a6d97424674b6f32b326e6d8d20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdc52de1727f4f6ea7904532f2ee91fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/669k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe13d02921f54b108b1f8e614cdbae30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "\n",
    "MODEL_NAME = \"profoz/distilbert-base-cased-finetuned-stars\"\n",
    "distilbert = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=5).to(0)\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "k0Ic_EZ6U7Ib",
   "metadata": {
    "id": "k0Ic_EZ6U7Ib"
   },
   "outputs": [],
   "source": [
    "distilbert.config.id2label = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dEcNEmHaUSnQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dEcNEmHaUSnQ",
    "outputId": "50bd1915-f7cb-4452-fd11-dfd9d03b4709"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'label': '0', 'score': 0.8580503463745117},\n",
       "  {'label': '1', 'score': 0.03356441855430603},\n",
       "  {'label': '2', 'score': 0.013912541791796684},\n",
       "  {'label': '3', 'score': 0.009098300710320473},\n",
       "  {'label': '4', 'score': 0.08537442237138748}]]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_pipeline = pipeline('text-classification', model=distilbert, tokenizer=tokenizer)\n",
    "bert_pipeline('I hate this thing', return_all_scores=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "U2BhJY_XUSi9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
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    "id": "U2BhJY_XUSi9",
    "outputId": "8bf0897d-a8d8-48ff-c91e-329c50cd9a01"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fa6a7564d8e45b0a5cb7ad16a4c00ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5762 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
     ]
    }
   ],
   "source": [
    "bert_data = {\n",
    "    'class_0_probs': [],\n",
    "    'class_1_probs': [],\n",
    "    'class_2_probs': [],\n",
    "    'class_3_probs': [],\n",
    "    'class_4_probs': [],\n",
    "    'actual_labels': []\n",
    "}\n",
    "\n",
    "for test in tqdm(test_sample): # I could do this in batches I know\n",
    "    try:\n",
    "        probs = bert_pipeline(test['review'], return_all_scores=True)[0]\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "    for prob in probs:\n",
    "        bert_data[f'class_{prob[\"label\"]}_probs'].append(prob['score'])\n",
    "    bert_data['actual_labels'].append(test['star'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ybyPYqDGUSfs",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "ybyPYqDGUSfs",
    "outputId": "61f5d9a9-4c50-4f3a-c462-ece715786066"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "# Example dataframe with probabilities for 5 classes and actual labels\n",
    "\n",
    "df = pd.DataFrame(bert_data)\n",
    "\n",
    "# Number of classes\n",
    "n_classes = 5\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Compute and plot calibration curve for each class\n",
    "for i in range(n_classes):\n",
    "    # Extract probabilities for the current class\n",
    "    probs = df[f'class_{i}_probs']\n",
    "    # Create binary labels for the current class\n",
    "    labels = (df['actual_labels'] == i).astype(int)\n",
    "\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=10, normalize=False)\n",
    "\n",
    "    # Plot\n",
    "    ax.plot(prob_pred, prob_true, marker='o', label=f'Class {i}')\n",
    "\n",
    "# Plot perfectly calibrated line\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n",
    "\n",
    "ax.set_xlabel(\"Mean predicted probability\")\n",
    "ax.set_ylabel(\"Fraction of positives\")\n",
    "ax.set_title(\"DistilBERT Calibration (FT)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('bert_scores.png', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yvy_OA1A9R-A",
   "metadata": {
    "id": "yvy_OA1A9R-A"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hGJhoogcUSVP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hGJhoogcUSVP",
    "outputId": "078097b0-17ac-44e4-9184-6a58e1b71a20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7087816730301979\n",
      "Log Loss: 0.828313703112158\n",
      "ECE per class: [0.007650817078975451, 0.005718088775521394, 0.0060034777801186495, 0.008513987193343765, 0.015525377459768473]\n",
      "Average ECE: 0.008682349657545546\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "HcW9IKjS8nBp",
   "metadata": {
    "id": "HcW9IKjS8nBp"
   },
   "outputs": [],
   "source": [
    "df['actual_labels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IUr7jkTUf2_Y",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "IUr7jkTUf2_Y",
    "outputId": "bb4e59bb-a94a-4b5e-a8ba-32314084fa20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='actual_labels'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['actual_labels'].value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "RMVzdkIMf3iR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RMVzdkIMf3iR",
    "outputId": "daf2dabc-2aff-4aa5-d0ec-ffd2aafb8e47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['package_name', 'review', 'date', 'star'],\n",
       "    num_rows: 172839\n",
       "})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "S8YaY-7clRbg",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "S8YaY-7clRbg",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "4d038e40-fe6c-4721-9a63-786ba1e31b2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sentence-transformers\n",
      "  Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n",
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/227.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.41.2)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.4)\n",
      "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.3.0+cu121)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.25.2)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.2.2)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.11.4)\n",
      "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.23.3)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (9.4.0)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (3.14.0)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2023.6.0)\n",
      "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (24.1)\n",
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      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2.32.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (4.12.2)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.12.1)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.3)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
      "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
      "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
      "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
      "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
      "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
      "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
      "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
      "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
      "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
      "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
      "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
      "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
      "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (2.3.0)\n",
      "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m72.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (2024.5.15)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.19.1)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.4.3)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.6.2)\n",
      "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
      "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, sentence-transformers\n",
      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 sentence-transformers-3.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "laj1lVathW6o",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "2774edf324f84a0e8fdb338a7130abcc",
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      "c0b96646fe0c4f958ac10afa1d74bf33",
      "422687997a5c49e7aa86b285b3fd9eb5",
      "6dd2de95305940939686f7e59e3f4e0a",
      "243a39c1373e40b1b475b6ea4f8ae91c",
      "2b57efae92a645f385e6344120332105",
      "c9a0fcc15efb44e5b964ba0f7d2e3fd3",
      "a793b075d689475d9fc674fb20841210",
      "e1fdab2ec08b45eca0a08b20bafaa332"
     ]
    },
    "id": "laj1lVathW6o",
    "outputId": "b4ee0ff8-cccf-4172-ff01-235d4fac48fc"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2774edf324f84a0e8fdb338a7130abcc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/5402 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(172839, 768)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\n",
    "\n",
    "docs = dataset['train']['review']\n",
    "doc_emb = model.encode(docs, batch_size=32, show_progress_bar=True)\n",
    "\n",
    "doc_emb.shape  #  == (n, 768)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "jMdoPELthXIr",
   "metadata": {
    "id": "jMdoPELthXIr"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "X5w9i86khXc_",
   "metadata": {
    "id": "X5w9i86khXc_"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "U72xH0dLhSTE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U72xH0dLhSTE",
    "outputId": "37ad0f48-c294-4a82-9948-373335341d35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Does what it should.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'package_name': 'com.colinmcdonough.android.torch',\n",
       "  'review': 'Does exactly what it should.',\n",
       "  'date': 'July 23 2015',\n",
       "  'star': 4},\n",
       " {'package_name': 'org.isoron.uhabits',\n",
       "  'review': 'Does what it should',\n",
       "  'date': 'October 23 2016',\n",
       "  'star': 4},\n",
       " {'package_name': 'de.danoeh.antennapod',\n",
       "  'review': 'Does what it should do',\n",
       "  'date': 'January 07 2017',\n",
       "  'star': 4},\n",
       " {'package_name': 'com.nolanlawson.logcat',\n",
       "  'review': 'Does what it is supposed to.',\n",
       "  'date': 'January 22 2017',\n",
       "  'star': 4},\n",
       " {'package_name': 'dev.ukanth.ufirewall',\n",
       "  'review': 'It does what it should be',\n",
       "  'date': 'February 02 2017',\n",
       "  'star': 4}]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import util\n",
    "\n",
    "k = 5\n",
    "\n",
    "print(test_sample[0]['review'])\n",
    "\n",
    "query_emb = model.encode(test_sample[0]['review'])\n",
    "scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()\n",
    "# Retrieve the corresponding examples from the dataset\n",
    "[dataset['train'][int(_)] for _ in np.argsort(scores)[-k:][::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36FflPxgiCkx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "29504d5d5d1b4f11912f82245d94edf1",
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      "de5d00967825419a809c783d278d83f1",
      "ae27f557fa294abd82d04e57eb6bf492"
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    },
    "collapsed": true,
    "id": "36FflPxgiCkx",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "233d6647-c404-42fb-c806-feb86120babf"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29504d5d5d1b4f11912f82245d94edf1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5762 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "few_shot_non_ft_gpt_3_5_data = {\n",
    "    'class_0_probs': [],\n",
    "    'class_1_probs': [],\n",
    "    'class_2_probs': [],\n",
    "    'class_3_probs': [],\n",
    "    'class_4_probs': [],\n",
    "    'actual_labels': [],\n",
    "    'predicted_token': []\n",
    "}\n",
    "\n",
    "for test in tqdm(test_sample):\n",
    "    _data = {}\n",
    "\n",
    "    query_emb = model.encode(test['review'])\n",
    "    scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()\n",
    "    # Retrieve the corresponding examples from the dataset\n",
    "    examples = [dataset['train'][int(_)] for _ in np.argsort(scores)[-k:][::-1]]\n",
    "    examples_str = '\\n--\\n'.join([f'Review: {e[\"review\"]}\\nRating: {e[\"star\"]}' for e in examples]).strip()\n",
    "    try:\n",
    "        predicted_label, probs = run_ft_model(\n",
    "            f'Please classify these app reviews with 0-4 \\n--\\n{examples_str}\\n--\\nReview: {test[\"review\"]}\\nRating:',\n",
    "            'gpt-3.5-turbo-0125', chat=True, system=''\n",
    "            )\n",
    "    except:\n",
    "        continue\n",
    "    for key, prob in probs.items():\n",
    "        if str(key.lower().strip()) in ['0', '1', '2', '3', '4'] and f'class_{key.lower().strip()}_probs' not in _data:\n",
    "            _data[f'class_{key.lower().strip()}_probs'] = prob\n",
    "    if len(_data) >= 5:\n",
    "        for key, value in _data.items():\n",
    "            few_shot_non_ft_gpt_3_5_data[key].append(value)\n",
    "        few_shot_non_ft_gpt_3_5_data['actual_labels'].append(test['star'])\n",
    "        few_shot_non_ft_gpt_3_5_data['predicted_token'].append(predicted_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb0i0fjeiIzv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "cb0i0fjeiIzv",
    "outputId": "7fa53857-9742-41b4-edc6-d653104f8bed"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example dataframe with probabilities for 5 classes and actual labels\n",
    "\n",
    "df = pd.DataFrame({k: v for k, v in few_shot_non_ft_gpt_3_5_data.items() if k != 'predicted_token'})\n",
    "\n",
    "# Number of classes\n",
    "n_classes = 5\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Compute and plot calibration curve for each class\n",
    "for i in range(n_classes):\n",
    "    # Extract probabilities for the current class\n",
    "    probs = df[f'class_{i}_probs']\n",
    "    # Create binary labels for the current class\n",
    "    labels = (df['actual_labels'] == i).astype(int)\n",
    "\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=10, normalize=False)\n",
    "\n",
    "    # Plot\n",
    "    ax.plot(prob_pred, prob_true, marker='o', label=f'Class {i}')\n",
    "\n",
    "# Plot perfectly calibrated line\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n",
    "\n",
    "ax.set_xlabel(\"Mean predicted probability\")\n",
    "ax.set_ylabel(\"Fraction of positives\")\n",
    "ax.set_title(\"GPT 3.5 0125 Calibration (non FT, 5-shot)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('gpt_5_shot.png', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "FCinrnAL6sXH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "FCinrnAL6sXH",
    "outputId": "e442749f-3dd0-449a-dc0d-31991451ef24"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can only compare identically-labeled Series objects",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-f3524b693760>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfew_shot_non_ft_gpt_3_5_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predicted_token'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfew_shot_non_ft_gpt_3_5_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'actual_labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/arraylike.py\u001b[0m in \u001b[0;36m__eq__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__eq__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__eq__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cmp_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__ne__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_cmp_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   6088\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6089\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_indexed_same\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6090\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only compare identically-labeled Series objects\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6092\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Can only compare identically-labeled Series objects"
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    }
   ],
   "source": [
    "pd.Series(few_shot_non_ft_gpt_3_5_data['predicted_token']) == pd.Series(few_shot_non_ft_gpt_3_5_data['actual_labels'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "MCAKVITuiIwn",
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    },
    "id": "MCAKVITuiIwn",
    "outputId": "620972a6-f261-4a03-bc42-d58519a82ee0"
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (top token): 0.5242255758538522\n",
      "Log Loss: 2.2580764282857\n",
      "ECE per class: [0.09493193010325662, 0.06264184074662434, 0.09240138204924542, 0.1563492930897538, 0.25714156076250994]\n",
      "Average ECE: 0.132693201350278\n"
     ]
    }
   ],
   "source": [
    "# Predicted probabilities for each class\n",
    "probs = df[[f'class_{i}_probs' for i in range(n_classes)]].values\n",
    "accuracy = (probs.argmax(-1) == df['actual_labels']).mean()\n",
    "\n",
    "print(f'Accuracy (top token): {accuracy}')\n",
    "# a = (pd.Series(few_shot_non_ft_gpt_3_5_data['predicted_token']) == df['actual_labels'].astype(str)[:len(few_shot_non_ft_gpt_3_5_data['predicted_token'])]).mean()\n",
    "# print(f'Accuracy (generated): {a}')\n",
    "# Calculate log loss\n",
    "log_loss_value = log_loss(df['actual_labels'], probs)\n",
    "\n",
    "print(f\"Log Loss: {log_loss_value}\")\n",
    "\n",
    "def calculate_ece(probs, labels, n_bins=10):\n",
    "    # Calculate the calibration curve\n",
    "    prob_true, prob_pred = calibration_curve(labels, probs, n_bins=n_bins, normalize=False)\n",
    "\n",
    "    # Calculate the histogram of probabilities\n",
    "    bin_counts, bin_edges = np.histogram(probs, bins=n_bins, range=(0,1))\n",
    "    bin_widths = np.diff(bin_edges)\n",
    "\n",
    "    # Align the bin counts with the number of points used in each bin in the calibration curve\n",
    "    # This is needed because calibration_curve may return fewer bins if not enough samples\n",
    "    actual_bins_used = len(prob_true)\n",
    "    if actual_bins_used < n_bins:\n",
    "        bin_counts = bin_counts[:actual_bins_used]\n",
    "\n",
    "    # Calculate ECE as the weighted average of absolute differences\n",
    "    ece = np.sum(np.abs(prob_pred - prob_true) * bin_counts) / np.sum(bin_counts)\n",
    "    return ece\n",
    "\n",
    "# Assuming you have a DataFrame `df` and a number of classes `n_classes`\n",
    "# ECE for each class\n",
    "eces = [calculate_ece(df[f'class_{i}_probs'], (df['actual_labels'] == i).astype(int), n_bins=10) for i in range(n_classes)]\n",
    "average_ece = np.mean(eces)\n",
    "\n",
    "print(f\"ECE per class: {eces}\")\n",
    "print(f\"Average ECE: {average_ece}\")"
   ]
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